@article{xing2002,
  title={Distance metric learning with application to clustering with side-information},
  author={Xing, Eric and Jordan, Michael and Russell, Stuart J and Ng, Andrew},
  journal={Advances in neural information processing systems},
  volume={15},
  year={2002}
}

@inproceedings{hadsell2006,
  title={Dimensionality reduction by learning an invariant mapping},
  author={Hadsell, Raia and Chopra, Sumit and LeCun, Yann},
  booktitle={2006 IEEE computer society conference on computer vision and pattern recognition (CVPR'06)},
  volume={2},
  pages={1735--1742},
  year={2006},
  organization={IEEE}
}

@inproceedings{javed2005,
  title={Appearance modeling for tracking in multiple non-overlapping cameras},
  author={Javed, Omar and Shafique, Khurram and Shah, Mubarak},
  booktitle={2005 IEEE computer society conference on computer vision and pattern recognition (CVPR'05)},
  volume={2},
  pages={26--33},
  year={2005},
  organization={IEEE}
}

#edge detection metric learning
@inproceedings{frome2007,
  title={Learning globally-consistent local distance functions for shape-based image retrieval and classification},
  author={Frome, Andrea and Singer, Yoram and Sha, Fei and Malik, Jitendra},
  booktitle={2007 IEEE 11th International Conference on Computer Vision},
  pages={1--8},
  year={2007},
  organization={IEEE}
}

@inproceedings{Hoffer2014,
  title={Deep metric learning using triplet network},
  author={Hoffer, Elad and Ailon, Nir},
  booktitle={Similarity-Based Pattern Recognition: Third International Workshop, SIMBAD 2015, Copenhagen, Denmark, October 12-14, 2015. Proceedings 3},
  pages={84--92},
  year={2015},
  organization={Springer}
}

@article{rippel2015,
  title={Metric learning with adaptive density discrimination},
  author={Rippel, Oren and Paluri, Manohar and Dollar, Piotr and Bourdev, Lubomir},
  journal={arXiv preprint arXiv:1511.05939},
  year={2015}
}

@article{sohn2016,
  title={Improved deep metric learning with multi-class n-pair loss objective},
  author={Sohn, Kihyuk},
  journal={Advances in neural information processing systems},
  volume={29},
  year={2016}
}

@inproceedings{oh2016,
  title={Deep metric learning via lifted structured feature embedding},
  author={Song, Hyun and Xiang, Yu and Jegelka, Stefanie and Savarese, Silvio},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={4004--4012},
  year={2016}
}

@inproceedings{Bai2017,
  title={Incorporating intra-class variance to fine-grained visual recognition},
  author={Em, Yan and Gag, Feng and Lou, Yihang and Wang, Shiqi and Huang, Tiejun and Duan, Ling-Yu},
  booktitle={2017 IEEE International Conference on Multimedia and Expo (ICME)},
  pages={1452--1457},
  year={2017},
  organization={IEEE}
}

@article{Li2019,
    abstract = {Deep learning is a growing trend in medical image analysis. There are limited data of deep learning techniques applied in Chest X-rays. This paper proposed a deep learning algorithm for cardiothoracic ratio (CTR) calculation in chest X-rays. A fully convolutional neural network was employed to segment chest X-ray images and calculate CTR. CTR values derived from the deep learning model were compared with the reference standard using Bland-Altman analysis and linear correlation graphs, and intra-class correlation (ICC) analyses. Diagnostic performance of the model for the detection of heart enlargement was assessed and compared with other deep learning methods and radiologists. CTR values derived from the deep learning method showed excellent agreement with the reference standard, with mean difference 0.0004 ± 0.0133, 95},
    author = {Zhennan Li and Zhihui Hou and Chen Chen and Zhi Hao and Yunqiang An and Sen Liang and Bin Lu},
    doi = {10.1109/ACCESS.2019.2900053},
    issn = {21693536},
    journal = {IEEE Access},
    keywords = {Machine learning,X-rays application,cardiomegaly,deep learning,object segmentation},
    pages = {37749-37756},
    publisher = {Institute of Electrical and Electronics Engineers Inc.},
    title = {Automatic Cardiothoracic Ratio Calculation with Deep Learning},
    volume = {7},
    year = {2019},
 }

@inproceedings{Musgrave2020,
  title={A metric learning reality check},
  author={Musgrave, Kevin and Belongie, Serge and Lim, Ser-Nam},
  booktitle={Computer Vision--ECCV 2020: 16th European Conference, Glasgow, UK, August 23--28, 2020, Proceedings, Part XXV 16},
  pages={681--699},
  year={2020},
  organization={Springer}
}


@article{Ni2017,
    abstract = {Patient similarity measuring1 plays a significant role in many healthcare applications, such as cohort study and treatment comparative effectiveness research. Existing methods mainly rely on supervised metric learning method to study patient similarity from Electronic Health Records (EHRs), facing the challenge of differentiating patients with a large number of finegrained disease categories. Deep metric learning has gained noticeable success in fine-grained image categorization problem, however, it cannot be directly applied to classification of patients with hierarchical disease labels. In this paper, we present a novel three layer patient similarity deep metric learning framework (PSDML) by optimizing quadruple loss improved from triplet loss, to learn an embedding distance for disease classification among the patients. The context semantic relation of multi diagnosis labels encoding by ICD-10 is taken into account to compute the supervised distance of patients. To solve the diagnosis class imbalance, patient tuples that violate deep metric learning framework loss constraints are chosen prior as samples to accelerate the convergence of the neural network. We conducted KNN multi label classification experiment using the learned similarity metric on the real EHRs about stroke disease collected by Chinese Stroke Data Center. The results demonstrate substantial improvement over the baselines.},
   author = {Jiazhi Ni and Jie Liu and Chenxin Zhang and Dan Ye and Zhirou Ma},
    doi = {10.1145/3132847.3133022},
    isbn = {9781450349185},
    journal = {International Conference on Information and Knowledge Management, Proceedings},
    keywords = {Deep metric learning,Distance metric learning,Multi label classification,Patient similarity},
    month = {11},
    pages = {1189-1198},
    publisher = {Association for Computing Machinery},
    title = {Fine-grained patient similarity measuring using deep metric learning},
    volume = {Part F131841},
    year = {2017},
 }

@article{bromley1993signature,
  title={Signature verification using a" siamese" time delay neural network},
  author={Bromley, Jane and Guyon, Isabelle and LeCun, Yann and S{\"a}ckinger, Eduard and Shah, Roopak},
  journal={Advances in neural information processing systems},
  volume={6},
  year={1993}
}


@Article{sym11091066,
 AUTHOR = {Kaya, Mahmut and Bilge, Hasan Şakir},
 TITLE = {Deep Metric Learning: A Survey},
 JOURNAL = {Symmetry},
 VOLUME = {11},
 YEAR = {2019},
 NUMBER = {9},
 ARTICLE-NUMBER = {1066},
 ISSN = {2073-8994},
 ABSTRACT = {Metric learning},
 DOI = {10.3390/sym11091066}
}

@article{APICELLA202114,
title = {A survey on modern trainable activation functions},
journal = {Neural Networks},
volume = {138},
pages = {14-32},
year = {2021},
issn = {0893-6080},
doi = {https://doi.org/10.1016/j.neunet.2021.01.026},
author = {Andrea Apicella and Francesco Donnarumma and Francesco Isgrò and Roberto Prevete},
keywords = {Neural networks, Machine learning, Activation functions, Trainable activation functions, Learnable activation functions},
abstract = {.}
}

@article{hubel1962receptive,
  title={Receptive fields, binocular interaction and functional architecture in the cat's visual cortex},
  author={Hubel, David H and Wiesel, Torsten N},
  journal={The Journal of physiology},
  volume={160},
  number={1},
  pages={106},
  year={1962},
  publisher={Wiley-Blackwell}
}

@article{fukushima1980neocognitron,
  title={Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position},
  author={Fukushima, Kunihiko},
  journal={Biological cybernetics},
  volume={36},
  number={4},
  pages={193--202},
  year={1980},
  publisher={Springer}
}

@ARTICLE{lecun1998,
  author={Lecun, Y. and Bottou, L. and Bengio, Y. and Haffner, P.},
  journal={Proceedings of the IEEE}, 
  title={Gradient-based learning applied to document recognition}, 
  year={1998},
  volume={86},
  number={11},
  pages={2278-2324},
  doi={10.1109/5.726791}}

@article{simonyan2014very,
  title={Very deep convolutional networks for large-scale image recognition},
  author={Simonyan, Karen and Zisserman, Andrew},
  journal={arXiv preprint arXiv:1409.1556},
  year={2014}
}
@inproceedings{he2016deep,
  title={Deep residual learning for image recognition},
  author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={770--778},
  year={2016}
}

@inproceedings{szegedy2015going,
  title={Going deeper with convolutions},
  author={Szegedy, Christian and Liu, Wei and Jia, Yangqing and Sermanet, Pierre and Reed, Scott and Anguelov, Dragomir and Erhan, Dumitru and Vanhoucke, Vincent and Rabinovich, Andrew},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={1--9},
  year={2015}
}

@inproceedings{huang2017densely,
  title={Densely connected convolutional networks},
  author={Huang, Gao and Liu, Zhuang and Van Der Maaten, Laurens and Weinberger, Kilian Q},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={4700--4708},
  year={2017}
}

@article{krizhevsky2012imagenet,
  title={Imagenet classification with deep convolutional neural networks},
  author={Krizhevsky, Alex and Sutskever, Ilya and Hinton, Geoffrey E},
  journal={Advances in neural information processing systems},
  volume={25},
  year={2012}
}

@inproceedings{ying2019overview,
  title={An overview of overfitting and its solutions},
  author={Ying, Xue},
  booktitle={Journal of physics: Conference series},
  volume={1168},
  pages={022022},
  year={2019},
  organization={IOP Publishing}
}

@article{rumelhart1986learning,
  title={Learning representations by back-propagating errors},
  author={Rumelhart, David E and Hinton, Geoffrey E and Williams, Ronald J},
  journal={nature},
  volume={323},
  number={6088},
  pages={533--536},
  year={1986},
  publisher={Nature Publishing Group UK London}
}

@article{hornik1989multilayer,
  title={Multilayer feedforward networks are universal approximators},
  author={Hornik, Kurt and Stinchcombe, Maxwell and White, Halbert},
  journal={Neural networks},
  volume={2},
  number={5},
  pages={359--366},
  year={1989},
  publisher={Elsevier}
}

@INPROCEEDINGS{Yi2014,
  author={Yi, Dong and Lei, Zhen and Liao, Shengcai and Li, Stan Z.},
  booktitle={2014 22nd International Conference on Pattern Recognition}, 
  title={Deep Metric Learning for Person Re-identification}, 
  year={2014},
  volume={},
  number={},
  pages={34-39},
  doi={10.1109/ICPR.2014.16}}

@article{khosla2020,
  title={Supervised contrastive learning},
  author={Khosla, Prannay and Teterwak, Piotr and Wang, Chen and Sarna, Aaron and Tian, Yonglong and Isola, Phillip and Maschinot, Aaron and Liu, Ce and Krishnan, Dilip},
  journal={Advances in neural information processing systems},
  volume={33},
  pages={18661--18673},
  year={2020}
}

@article{hermans2017,
  title={In defense of the triplet loss for person re-identification},
  author={Hermans, Alexander and Beyer, Lucas and Leibe, Bastian},
  journal={arXiv preprint arXiv:1703.07737},
  year={2017}
}

@inproceedings{cakir2019,
  title={Deep metric learning to rank},
  author={Cakir, Fatih and He, Kun and Xia, Xide and Kulis, Brian and Sclaroff, Stan},
  booktitle={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition},
  pages={1861--1870},
  year={2019}
}

@inproceedings{chen2020simclr,
  title={A simple framework for contrastive learning of visual representations},
  author={Chen, Ting and Kornblith, Simon and Norouzi, Mohammad and Hinton, Geoffrey},
  booktitle={International conference on machine learning},
  pages={1597--1607},
  year={2020},
  organization={PMLR}
}

@article{johnson2019mimic,
  title={MIMIC-CXR, a de-identified publicly available database of chest radiographs with free-text reports},
  author={Johnson, Alistair EW and Pollard, Tom J and Berkowitz, Seth J and Greenbaum, Nathaniel R and Lungren, Matthew P and Deng, Chih-ying and Mark, Roger G and Horng, Steven},
  journal={Scientific data},
  volume={6},
  number={1},
  pages={317},
  year={2019},
  publisher={Nature Publishing Group UK London}
}

@inproceedings{wang2017chestx,
  title={Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases},
  author={Wang, Xiaosong and Peng, Yifan and Lu, Le and Lu, Zhiyong and Bagheri, Mohammadhadi and Summers, Ronald M},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={2097--2106},
  year={2017}
}

@inproceedings{irvin2019chexpert,
  title={Chexpert: A large chest radiograph dataset with uncertainty labels and expert comparison},
  author={Irvin, Jeremy and Rajpurkar, Pranav and Ko, Michael and Yu, Yifan and Ciurea-Ilcus, Silviana and Chute, Chris and Marklund, Henrik and Haghgoo, Behzad and Ball, Robyn and Shpanskaya, Katie and others},
  booktitle={Proceedings of the AAAI conference on artificial intelligence},
  volume={33},
  pages={590--597},
  year={2019}
}

@inproceedings{deng2009imagenet,
  title={Imagenet: A large-scale hierarchical image database},
  author={Deng, Jia and Dong, Wei and Socher, Richard and Li, Li-Jia and Li, Kai and Fei-Fei, Li},
  booktitle={2009 IEEE conference on computer vision and pattern recognition},
  pages={248--255},
  year={2009},
  organization={Ieee}
}

@inproceedings{tan2019efficientnet,
  title={Efficientnet: Rethinking model scaling for convolutional neural networks},
  author={Tan, Mingxing and Le, Quoc},
  booktitle={International conference on machine learning},
  pages={6105--6114},
  year={2019},
  organization={PMLR}
}

@article{howard2017mobilenets,
  title={Mobilenets: Efficient convolutional neural networks for mobile vision applications},
  author={Howard, Andrew G and Zhu, Menglong and Chen, Bo and Kalenichenko, Dmitry and Wang, Weijun and Weyand, Tobias and Andreetto, Marco and Adam, Hartwig},
  journal={arXiv preprint arXiv:1704.04861},
  year={2017}
}

@inproceedings{sandler2018mobilenetv2,
  title={Mobilenetv2: Inverted residuals and linear bottlenecks},
  author={Sandler, Mark and Howard, Andrew and Zhu, Menglong and Zhmoginov, Andrey and Chen, Liang-Chieh},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={4510--4520},
  year={2018}
}

@article{montalbo2022,
author = {Montalbo, Francis Jesmar},
year = {2022},
month = {01},
pages = {},
title = {Automated Diagnosis of Diverse Coffee Leaf Images through a Stage-Wise Aggregated Triple Deep Convolutional Neural Network},
volume = {33},
journal = {Machine Vision and Applications},
doi = {10.1007/s00138-022-01277-y}
}

@inproceedings{tan2021efficientnetv2,
  title={Efficientnetv2: Smaller models and faster training},
  author={Tan, Mingxing and Le, Quoc},
  booktitle={International conference on machine learning},
  pages={10096--10106},
  year={2021},
  organization={PMLR}
}


% Ranking loss (1):
@article{ZhZ2013,
    address = {Los Alamitos, CA, USA},
    author = {Zhang, Min--Ling and Zhou, Zhi-Hua},
    journal = {{IEEE} Transactions on Knowledge and Data Engineering},
    publisher = {IEEE Computer Society},
    title = {{A Review On Multi--Label Learning Algorithms}},
    volume = {99},
    year = {2013}
}

% Ranking loss (2):
@article{ZhZ2006,
    author = {Zhang, Min Ling and Zhou, Zhi Hua},
    doi = {10.1109/TKDE.2006.162},
    issn = {10414347},
    journal = {IEEE Transactions on Knowledge and Data Engineering},
    keywords = {Backpropagation,Data mining,Functional genomics,Machine learning,Multilabel learning,Neural networks,Text categorization},
    month = oct,
    number = {10},
    pages = {1338--1351},
    title = {{Multilabel neural networks with applications to functional genomics and text categorization}},
    volume = {18},
    year = {2006}
}

% Ranking loss (3):
@inproceedings{YWK2017,
    arxivId = {1707.00418},
    author = {Yeh, Chih Kuan and Wu, Wei Chieh and Ko, Wei Jen and Wang, Yu Chiang Frank},
    booktitle = {31st AAAI Conference on Artificial Intelligence, AAAI 2017},
    eprint = {1707.00418},
    month = jul,
    pages = {2838--2844},
    title = {{Learning deep latent spaces for multi-label classification}},
    year = {2017}
}


@article{GF2014,
  title={Generative adversarial nets},
  author={Goodfellow, Ian and Pouget-Abadie, Jean and Mirza, Mehdi and Xu, Bing and Warde-Farley, David and Ozair, Sherjil and Courville, Aaron and Bengio, Yoshua},
  journal={Advances in neural information processing systems},
  volume={27},
  year={2014}
}

@article{karras2017progressive,
  title={Progressive growing of gans for improved quality, stability, and variation},
  author={Karras, Tero and Aila, Timo and Laine, Samuli and Lehtinen, Jaakko},
  journal={arXiv preprint arXiv:1710.10196},
  year={2017}
}

@inproceedings{karras2019style,
  title={A style-based generator architecture for generative adversarial networks},
  author={Karras, Tero and Laine, Samuli and Aila, Timo},
  booktitle={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition},
  pages={4401--4410},
  year={2019}
}

@inproceedings{karras2020improving,
  title={Analyzing and improving the image quality of stylegan},
  author={Karras, Tero and Laine, Samuli and Aittala, Miika and Hellsten, Janne and Lehtinen, Jaakko and Aila, Timo},
  booktitle={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition},
  pages={8110--8119},
  year={2020}
}

@article{lecun1998gradient,
  title={Gradient-based learning applied to document recognition},
  author={LeCun, Yann and Bottou, L{\'e}on and Bengio, Yoshua and Haffner, Patrick},
  journal={Proceedings of the IEEE},
  volume={86},
  number={11},
  pages={2278--2324},
  year={1998},
  publisher={Ieee}
}

@article{radford2015unsupervised,
  title={Unsupervised representation learning with deep convolutional generative adversarial networks},
  author={Radford, Alec and Metz, Luke and Chintala, Soumith},
  journal={arXiv preprint arXiv:1511.06434},
  year={2015}
}

@inproceedings{ioffe2015batch,
  title={Batch normalization: Accelerating deep network training by reducing internal covariate shift},
  author={Ioffe, Sergey and Szegedy, Christian},
  booktitle={International conference on machine learning},
  pages={448--456},
  year={2015},
  organization={pmlr}
}

@article{salimans2016improved,
  title={Improved techniques for training gans},
  author={Salimans, Tim and Goodfellow, Ian and Zaremba, Wojciech and Cheung, Vicki and Radford, Alec and Chen, Xi},
  journal={Advances in neural information processing systems},
  volume={29},
  year={2016}
}

@inproceedings{arjovsky2017wasserstein,
  title={Wasserstein generative adversarial networks},
  author={Arjovsky, Martin and Chintala, Soumith and Bottou, L{\'e}on},
  booktitle={International conference on machine learning},
  pages={214--223},
  year={2017},
  organization={PMLR}
}

@book{villani2021topics,
  title={Topics in optimal transportation},
  author={Villani, C{\'e}dric},
  volume={58},
  year={2021},
  publisher={American Mathematical Soc.}
}

@article{gulrajani2017improved,
  title={Improved training of wasserstein gans},
  author={Gulrajani, Ishaan and Ahmed, Faruk and Arjovsky, Martin and Dumoulin, Vincent and Courville, Aaron C},
  journal={Advances in neural information processing systems},
  volume={30},
  year={2017}
}

@article{miyato2018spectral,
  title={Spectral normalization for generative adversarial networks},
  author={Miyato, Takeru and Kataoka, Toshiki and Koyama, Masanori and Yoshida, Yuichi},
  journal={arXiv preprint arXiv:1802.05957},
  year={2018}
}



@article{Kolossvary23,
author = {Kolossv\'{a}ry, M\'{a}rton and Raghu, Vineet                     K. and Nagurney, John                             T. and Hoffmann, Udo and Lu, Michael                         T.},
title = {Deep Learning Analysis of Chest Radiographs to Triage Patients with                     Acute Chest Pain Syndrome},
journal = {Radiology},
volume = {000},
number = {0},
pages = {221926},
year = {2023},
doi = {10.1148/radiol.221926},
    note ={PMID: 36648346},
eprint = { 
        https://doi.org/10.1148/radiol.221926
}
}

@article{MMS2020,
author = {Majkowska, Anna and Mittal, Sid and Steiner, David F. and Reicher, Joshua J. and McKinney, Scott Mayer and Duggan, Gavin E. and Eswaran, Krish and Cameron Chen, Po-Hsuan and Liu, Yun and Kalidindi, Sreenivasa Raju and Ding, Alexander and Corrado, Greg S. and Tse, Daniel and Shetty, Shravya},
title = {Chest Radiograph Interpretation with Deep Learning Models: Assessment with Radiologist-adjudicated Reference Standards and Population-adjusted Evaluation},
journal = {Radiology},
volume = {294},
number = {2},
pages = {421-431},
year = {2020},
doi = {10.1148/radiol.2019191293},
    note ={PMID: 31793848},
eprint = {https://doi.org/10.1148/radiol.2019191293}
}

@inproceedings{macpherson2022assessing,
  title={Assessing the Performance of Automated Prediction and Ranking of Patient Age from Chest X-rays Against Clinicians},
  author={MacPherson, Matthew and Muthuswamy, Keerthini and Amlani, Ashik and Hutchinson, Charles and Goh, Vicky and Montana, Giovanni},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={255--265},
  year={2022},
  organization={Springer}
}

@article{macpherson2023patient,
  title={Patient Reidentification from Chest Radiographs: An Interpretable Deep Metric Learning Approach and Its Applications},
  author={Macpherson, Matthew S and Hutchinson, Charles E and Horst, Carolyn and Goh, Vicky and Montana, Giovanni},
  journal={Radiology: Artificial Intelligence},
  pages={e230019},
  year={2023},
  publisher={Radiological Society of North America}
}


@inproceedings{WPL2017,
  title={Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases},
  author={Wang, Xiaosong and Peng, Yifan and Lu, Le and Lu, Zhiyong and Bagheri, Mohammadhadi and Summers, Ronald M},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={2097--2106},
  year={2017}
}

% PadChest
@article{BPS2020,
    archivePrefix = {arXiv},
    arxivId = {1901.07441},
    author = {Bustos, Aurelia and Pertusa, Antonio and Salinas, Jose Maria and de la Iglesia-Vay{\'{a}}, Maria},
    doi = {10.1016/j.media.2020.101797},
    eprint = {1901.07441},
    issn = {13618423},
    journal = {Medical Image Analysis},
    keywords = {Anatomical locations,Deep neural networks,Differential diagnoses,Radiographic findings,X-Ray image dataset},
    month = dec,
    pages = {101797},
    pmid = {32877839},
    publisher = {Elsevier B.V.},
    title = {{PadChest: A large chest x-ray image dataset with multi-label annotated reports}},
    volume = {66},
    year = {2020}
}



% MIMIC-CXR
@article{JPB2019,
    author = {Johnson, Alistair E. W. and Pollard, Tom J. and Berkowitz, Seth J. and Greenbaum, Nathaniel R. and Lungren, Matthew P. and Deng, Chih-ying and Mark, Roger G. and Horng, Steven},
    doi = {10.1038/s41597-019-0322-0},
    issn = {2052-4463},
    journal = {Scientific Data},
    month = dec,
    number = {1},
    pages = {317},
    pmid = {31831740},
    title = {{MIMIC-CXR, a de-identified publicly available database of chest radiographs with free-text reports}},
    volume = {6},
    year = {2019}
}

% ASMLC
@InProceedings{KNW2019,
    author="Kurmann, Thomas and M{\'a}rquez-Neila, Pablo and Wolf, Sebastian and Sznitman, Raphael",
    editor="Shen, Dinggang and Liu, Tianming and Peters, Terry M. and Staib, Lawrence H. and Essert, Caroline and Zhou, Sean and Yap, Pew-Thian and Khan, Ali",
    title="Deep Multi-label Classification in Affine Subspaces",
    booktitle="Medical Image Computing and Computer Assisted Intervention -- MICCAI 2019",
    year="2019",
     publisher="Springer International Publishing",
     address="Cham",
    pages="165--173",
    isbn="978-3-030-32239-7"
}

% MPVAE
@inproceedings{BKG2020,
  title     = {Disentangled Variational Autoencoder based Multi-Label Classification with Covariance-Aware Multivariate Probit Model},
  author    = {Bai, Junwen and Kong, Shufeng and Gomes, Carla},
  booktitle = {Proceedings of the Twenty-Ninth International Joint Conference on
               Artificial Intelligence, {IJCAI-20}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},             
  editor    = {Christian Bessiere},	
  pages     = {4313--4321},
  year      = {2020},
  month     = {7},
   note      = {Special track on AI for CompSust and Human well-being},
  doi       = {10.24963/ijcai.2020/595},
}


@incollection{PaW2019,
  title={A Year in Review: Identifying Delays in Lung Cancer Diagnosis},
  author={Patel, P and Davis, RM and Whitten, PE},
  booktitle={B38. LUNG CANCER: SCREENING PRACTICES AND TREATMENT OUTCOMES},
  pages={A3055--A3055},
  year={2019},
  publisher={American Thoracic Society}
}

@article{WHO2016,
	author = {World Health Organization},
	title = {Communicating radiation risks in paediatric imaging: information to support health care discussions about benefit and risk},
	year = {2016},
	pages = {},
    journal={World Health Organization},
	publisher = {World Health Organization},
	type = {Publications}
}



@article{CSA2020,
  title={Chest x-ray in the COVID-19 pandemic: Radiologists’ real-world reader performance},
  author={Cozzi, Andrea and Schiaffino, Simone and Arpaia, Francesco and Della Pepa, Gianmarco and Tritella, Stefania and Bertolotti, Pietro and Menicagli, Laura and Monaco, Cristian Giuseppe and Carbonaro, Luca Alessandro and Spairani, Riccardo and others},
  journal={European journal of radiology},
  volume={132},
  pages={109272},
  year={2020},
  publisher={Elsevier}
}

% Inception:
@inproceedings{SLJ2015,
  title={Going deeper with convolutions},
  author={Szegedy, Christian and Liu, Wei and Jia, Yangqing and Sermanet, Pierre and Reed, Scott and Anguelov, Dragomir and Erhan, Dumitru and Vanhoucke, Vincent and Rabinovich, Andrew},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={1--9},
  year={2015}
}

% InceptionV3:
@inproceedings{SVI2016,
author = {Szegedy, Christian and Vanhoucke, Vincent and Ioffe, Sergey and Shlens, Jonathon and Wojna, Zbigniew},
booktitle = {Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition},
doi = {10.1109/CVPR.2016.308},
eprint = {1512.00567},
isbn = {9781467388504},
issn = {10636919},
month = dec,
pages = {2818--2826},
title = {{Rethinking the Inception Architecture for Computer Vision}},
volume = {2016-Decem},
year = {2016}
}

% Densenet:
@inproceedings{HLV2017,
author = {Huang, Gao and Liu, Zhuang and {Van Der Maaten}, Laurens and Weinberger, Kilian Q},
booktitle = {Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017},
doi = {10.1109/CVPR.2017.243},
eprint = {1608.06993},
isbn = {9781538604571},
pages = {2261--2269},
title = {{Densely connected convolutional networks}},
volume = {2017-Janua},
year = {2017}
}


% ResNET50:
@inproceedings{HZR2016,
author = {He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
booktitle = {Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition},
doi = {10.1109/CVPR.2016.90},
eprint = {1512.03385},
isbn = {9781467388504},
issn = {10636919},
title = {{Deep residual learning for image recognition}},
year = {2016}
}

% VGG:
@article{SiZ2014,
  title={Very deep convolutional networks for large-scale image recognition},
  author={Simonyan, Karen and Zisserman, Andrew},
  journal={arXiv:1409.1556},
  year={2014}
}

% AlexNet:
@article{KSH2012,
  title={Imagenet classification with deep convolutional neural networks},
  author={Krizhevsky, Alex and Sutskever, Ilya and Hinton, Geoffrey E},
  journal={Advances in neural information processing systems},
  volume={25},
  pages={1097--1105},
  year={2012}
}

% Lancet paper:
@article{STB2021,
  title={Effect of a comprehensive deep-learning model on the accuracy of chest x-ray interpretation by radiologists: a retrospective, multireader multicase study},
  author={Seah, Jarrel CY and Tang, Cyril HM and Buchlak, Quinlan D and Holt, Xavier G and Wardman, Jeffrey B and Aimoldin, Anuar and Esmaili, Nazanin and Ahmad, Hassan and Pham, Hung and Lambert, John F and others},
  journal={The Lancet Digital Health},
  volume={3},
  number={8},
  pages={e496--e506},
  year={2021},
  publisher={Elsevier}
}

% CheXtransfer
@inproceedings{KEB2021,
author = {Ke, Alexander and Ellsworth, William and Banerjee, Oishi and Ng, Andrew Y and Rajpurkar, Pranav},
booktitle = {ACM CHIL 2021 - Proceedings of the 2021 ACM Conference on Health, Inference, and Learning},
doi = {10.1145/3450439.3451867},
eprint = {2101.06871},
isbn = {9781450383592},
keywords = {ImageNet,chest X-ray interpretation,efficiency,generalization,pretraining,truncation},
month = jan,
pages = {116--124},
title = {{CheXtransfer: Performance and parameter efficiency of ImageNet models for chest X-Ray interpretation}},
year = {2021}
}


% Radiology paper:
@article{AWB2019,
author = {Annarumma, Mauro and Withey, Samuel J. and Bakewell, Robert J. and Pesce, Emanuele and Goh, Vicky and Montana, Giovanni},
doi = {10.1148/radiol.2019194005},
issn = {0033-8419},
journal = {Radiology},
month = apr,
number = {1},
pages = {272--272},
title = {{Automated Triaging of Adult Chest Radiographs with Deep Artificial Neural Networks}},
volume = {291},
year = {2019}
}

%Attention is all you need
@inproceedings{VSP2017,
    author = {Vaswani, Ashish and Shazeer, Noam and Parmar, Niki and Uszkoreit, Jakob and Jones, Llion and Gomez, Aidan N and Kaiser, \L ukasz and Polosukhin, Illia},
    booktitle = {Advances in Neural Information Processing Systems},
    editor = {I. Guyon and U. V. Luxburg and S. Bengio and H. Wallach and R. Fergus and S. Vishwanathan and R. Garnett},
    pages = {5998--6008},
    publisher = {Curran Associates, Inc.},
    title = {Attention is All you Need},
    volume = {30},
    year = {2017}
}

%BERT
@inproceedings{DCL2019,
    title = "{BERT}: Pre-training of Deep Bidirectional Transformers for Language Understanding",
    author = "Devlin, Jacob  and
      Chang, Ming-Wei  and
      Lee, Kenton  and
      Toutanova, Kristina",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
     address = "Minneapolis, Minnesota",
     publisher = "Association for Computational Linguistics",
    doi = "10.18653/v1/N19-1423",
    pages = "4171--4186",
}


@article{LCG2020,
	title = {{ALBERT}: {A} {Lite} {BERT} for {Self}-supervised {Learning} of {Language} {Representations}},
	shorttitle = {{ALBERT}},
	language = {en},
	journal = {arXiv:1909.11942},
	author = {Lan, Zhenzhong and Chen, Mingda and Goodman, Sebastian and Gimpel, Kevin and Sharma, Piyush and Soricut, Radu},
	month = feb,
	year = {2020},
	 note = {arXiv: 1909.11942},
	keywords = {Computer Science - Computation and Language, Computer Science - Artificial Intelligence}
}

% RoBERTa
@article{LOG2019,
	title = {{RoBERTa}: {A} {Robustly} {Optimized} {BERT} {Pretraining} {Approach}},
	shorttitle = {{RoBERTa}},
	language = {en},
	journal = {arXiv:1907.11692},
	author = {Liu, Yinhan and Ott, Myle and Goyal, Naman and Du, Jingfei and Joshi, Mandar and Chen, Danqi and Levy, Omer and Lewis, Mike and Zettlemoyer, Luke and Stoyanov, Veselin},
	month = jul,
	year = {2019},
	 note = {arXiv: 1907.11692},
	keywords = {Computer Science - Computation and Language}
}




@article{LYK2019,
  title={BioBERT: a pre-trained biomedical language representation model for biomedical text mining},
  author={Lee, Jinhyuk and Yoon, Wonjin and Kim, Sungdong and Kim, Donghyeon and Kim, Sunkyu and So, Chan Ho and Kang, Jaewoo},
  journal={Bioinformatics},
  volume={36},
  number={4},
  pages={1234--1240},
  year={2020},
  publisher={Oxford University Press}
}


@article{AMB2019,
	title = {Publicly {Available} {Clinical} {BERT} {Embeddings}},
	language = {en},
	journal = {arXiv:1904.03323},
	author = {Alsentzer, Emily and Murphy, John R. and Boag, Willie and Weng, Wei-Hung and Jin, Di and Naumann, Tristan and McDermott, Matthew B. A.},
	month = jun,
	year = {2019},
	 note = {arXiv: 1904.03323},
	keywords = {Computer Science - Computation and Language}
}
@inproceedings{burges2005learning,
  title={Learning to rank using gradient descent},
  author={Burges, Chris and Shaked, Tal and Renshaw, Erin and Lazier, Ari and Deeds, Matt and Hamilton, Nicole and Hullender, Greg},
  booktitle={Proceedings of the 22nd international conference on Machine learning},
  pages={89--96},
  year={2005}
}


@article{ai_replace,
author = {Langlotz, Curtis P.},
title = {Will Artificial Intelligence Replace Radiologists?},
journal = {Radiology: Artificial Intelligence},
volume = {1},
number = {3},
pages = {e190058},
year = {2019},
doi = {10.1148/ryai.2019190058},
    note ={PMID: 33937794},
eprint = { 
        https://doi.org/10.1148/ryai.2019190058
}
}

@article{JPS2016,
	title = {{MIMIC}-{III}, a freely accessible critical care database},
	volume = {3},
	issn = {2052-4463},
	doi = {10.1038/sdata.2016.35},
	language = {en},
	number = {1},
	journal = {Scientific Data},
	author = {Johnson, Alistair E.W. and Pollard, Tom J. and Shen, Lu and Lehman, Li-wei H. and Feng, Mengling and Ghassemi, Mohammad and Moody, Benjamin and Szolovits, Peter and Anthony Celi, Leo and Mark, Roger G.},
	month = dec,
	year = {2016},
	pages = {160035}
}


@article{PYL2019,
	title = {Transfer {Learning} in {Biomedical} {Natural} {Language} {Processing}: {An} {Evaluation} of {BERT} and {ELMo} on {Ten} {Benchmarking} {Datasets}},
	shorttitle = {Transfer {Learning} in {Biomedical} {Natural} {Language} {Processing}},
	language = {en},
	journal = {arXiv:1906.05474},
	author = {Peng, Yifan and Yan, Shankai and Lu, Zhiyong},
	month = jun,
	year = {2019},
	 note = {arXiv: 1906.05474},
	keywords = {Computer Science - Computation and Language}
}


@article{RHZ2020,
  title={The Utility of General Domain Transfer Learning for Medical Language Tasks},
  author={Ranti, Daniel and Hanss, Katie and Zhao, Shan and Arvind, Varun and Titano, Joseph and Costa, Anthony and Oermann, Eric},
  journal={arXiv:2002.06670},
  year={2020}
}


@article{SJR2020,
	title = {{CheXbert}: {Combining} {Automatic} {Labelers} and {Expert} {Annotations} for {Accurate} {Radiology} {Report} {Labeling} {Using} {BERT}},
	shorttitle = {{CheXbert}},
	language = {en},
	journal = {arXiv:2004.09167},
	author = {Smit, Akshay and Jain, Saahil and Rajpurkar, Pranav and Pareek, Anuj and Ng, Andrew Y. and Lungren, Matthew P.},
	month = apr,
	year = {2020},
	 note = {arXiv: 2004.09167},
	keywords = {Computer Science - Computation and Language, Computer Science - Machine Learning, Computer Science - Information Retrieval}
}



@article{MGS2020,
  title={Self-Supervised Contextual Language Representation of Radiology Reports to Improve the Identification of Communication Urgency},
  author={Meng, Xing and Ganoe, Craig H and Sieberg, Ryan T and Cheung, Yvonne Y and Hassanpour, Saeed},
  journal={AMIA Summits on Translational Science Proceedings},
  volume={2020},
  pages={413},
  year={2020},
  publisher={American Medical Informatics Association}
}


@article{BAG2020,
  title={Highly accurate classification of chest radiographic reports using a deep learning natural language model pre-trained on 3.8 million text reports},
  author={Bressem, Keno K and Adams, Lisa C and Gaudin, Robert A and Tr{\"o}ltzsch, Daniel and Hamm, Bernd and Makowski, Marcus R and Sch{\"u}le, Chan-Yong and Vahldiek, Janis L and Niehues, Stefan M},
  journal={Bioinformatics},
  volume={36},
  number={21},
  pages={5255--5261},
  year={2020},
  publisher={Oxford University Press}
}


@inproceedings{MHW2020,
  title={Chexpert++: Approximating the chexpert labeler for speed, differentiability, and probabilistic output},
  author={McDermott, Matthew BA and Hsu, Tzu Ming Harry and Weng, Wei-Hung and Ghassemi, Marzyeh and Szolovits, Peter},
  booktitle={Machine Learning for Healthcare Conference},
  pages={913--927},
  year={2020},
  organization={PMLR}
}

% SoTA review:
@article{CSG2021,
    archivePrefix = {arXiv},
    arxivId = {2103.08700},
    author = {{\c{C}}allı, Erdi and Sogancioglu, Ecem and van Ginneken, Bram and van Leeuwen, Kicky G. and Murphy, Keelin},
    doi = {10.1016/j.media.2021.102125},
    eprint = {2103.08700},
    issn = {13618415},
    journal = {Medical Image Analysis},
    month = aug,
    pages = {102125},
    publisher = {Elsevier BV},
    title = {{Deep learning for chest X-ray analysis: A survey}},
    volume = {72},
    year = {2021}
}


%Label-dependency:
@article{PLT2021,
  title={{Interpreting chest X-rays via CNNs that exploit hierarchical disease dependencies and uncertainty labels}},
  author={Pham, Hieu H and Le, Tung T and Tran, Dat Q and Ngo, Dat T and Nguyen, Ha Q},
  journal={Neurocomputing},
  volume={437},
  pages={186--194},
  year={2021},
  publisher={Elsevier}
}

%Label-dependency:
@article{CLL2020,
  title={Label co-occurrence learning with graph convolutional networks for multi-label chest x-ray image classification},
  author={Chen, Bingzhi and Li, Jinxing and Lu, Guangming and Yu, Hongbing and Zhang, David},
  journal={IEEE journal of biomedical and health informatics},
  volume={24},
  number={8},
  pages={2292--2302},
  year={2020},
  publisher={IEEE}
}

%Label-dependency:
@inproceedings{CSG2020,
  title={Learning decision ensemble using a graph neural network for comorbidity aware chest radiograph screening},
  author={Chakravarty, Arunava and Sarkar, Tandra and Ghosh, Nirmalya and Sethuraman, Ramanathan and Sheet, Debdoot},
  booktitle={2020 42nd Annual International Conference of the IEEE Engineering in Medicine \& Biology Society (EMBC)},
  pages={1234--1237},
  year={2020},
  organization={IEEE}
}

% ImageNet:
@inproceedings{DDS2009,
  author={Deng, Jia and Dong, Wei and Socher, Richard and Li, Li-Jia and Kai Li and Li Fei-Fei},
  booktitle={2009 IEEE Conference on Computer Vision and Pattern Recognition}, 
  title={ImageNet: A large-scale hierarchical image database}, 
  year={2009},
  volume={},
  number={},
  pages={248-255},
  doi={10.1109/CVPR.2009.5206848}
}

% GradCAM:
@INPROCEEDINGS{SCD2017,
    author={Selvaraju, Ramprasaath R. and Cogswell, Michael and Das, Abhishek and Vedantam, Ramakrishna and Parikh, Devi and Batra, Dhruv},
    booktitle={2017 IEEE International Conference on Computer Vision (ICCV)},
    title={Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization},
    year={2017},
    volume={},
    number={},
    pages={618-626},
    doi={10.1109/ICCV.2017.74}
}


@article{RIB2018,
author = {Rajpurkar, Pranav and Irvin, Jeremy and Ball, Robyn L. and Zhu, Kaylie and Yang, Brandon and Mehta, Hershel and Duan, Tony and Ding, Daisy and Bagul, Aarti and Langlotz, Curtis P. and Patel, Bhavik N. and Yeom, Kristen W. and Shpanskaya, Katie and Blankenberg, Francis G. and Seekins, Jayne and Amrhein, Timothy J. and Mong, David A. and Halabi, Safwan S. and Zucker, Evan J. and Ng, Andrew Y. and Lungren, Matthew P.},
doi = {10.1371/journal.pmed.1002686},
editor = {Sheikh, Aziz},
issn = {1549-1676},
journal = {PLOS Medicine},
keywords = {Algorithms,Cancer detection and diagnosis,Deep learning,Diagnostic radiology,Hernia,Lung and intrathoracic tumors,Pneumonia,Radiologists},
month = nov,
number = {11},
pages = {e1002686},
publisher = {Public Library of Science},
title = {{Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists}},
volume = {15},
year = {2018}
}


@inproceedings{GGG2019,
archivePrefix = {arXiv},
arxivId = {1906.07775},
author = {Ghesu, Florin C. and Georgescu, Bogdan and Gibson, Eli and Guendel, Sebastian and Kalra, Mannudeep K. and Singh, Ramandeep and Digumarthy, Subba R. and Grbic, Sasa and Comaniciu, Dorin},
booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
doi = {10.1007/978-3-030-32226-7_75},
eprint = {1906.07775},
isbn = {9783030322250},
issn = {16113349},
month = oct,
pages = {676--684},
publisher = {Springer},
title = {{Quantifying and Leveraging Classification Uncertainty for Chest Radiograph Assessment}},
volume = {11769 LNCS},
year = {2019}
}

@article{PCL2019,
author = {Park, Beomhee and Cho, Yongwon and Lee, Gaeun and Lee, Sang Min and Cho, Young-Hoon and Lee, Eun Sol and Lee, Kyung Hee and Seo, Joon Beom and Kim, Namkug},
journal = {Scientific reports},
number = {1},
pages = {1--9},
publisher = {Nature Publishing Group},
title = {{A curriculum Learning Strategy to enhance the Accuracy of Classification of Various Lesions in chest-pA X-ray Screening for pulmonary Abnormalities}},
volume = {9},
year = {2019}
}


% NLTK:
@book{BKL2009,
  title={Natural language processing with Python: analyzing text with the natural language toolkit},
  author={Bird, Steven and Klein, Ewan and Loper, Edward},
  year={2009},
  publisher={" O'Reilly Media, Inc."}
}

@article{ZTL2020,
  title={Familial cluster of COVID-19 infection from an asymptomatic},
  author={Zhang, Jinjun and Tian, Sijia and Lou, Jing and Chen, Yuguo},
  journal={Critical care},
  volume={24},
  number={1},
  pages={1--3},
  year={2020},
  publisher={Springer}
}


@article{LZY2020,
author = {Yi Lei and Han-Wen Zhang and Juan Yu and Michael N. Patlas},
title ={COVID-19 Infection: Early Lessons},
journal = {Canadian Association of Radiologists Journal},
volume = {71},
number = {3},
pages = {251-252},
year = {2020},
doi = {10.1177/0846537120914428},
note ={PMID: 32162990},
eprint = {https://doi.org/10.1177/0846537120914428}
}


@article{BWA2015,
  title={Understanding and confronting our mistakes: the epidemiology of error in radiology and strategies for error reduction},
  author={Bruno, Michael A and Walker, Eric A and Abujudeh, Hani H},
  journal={Radiographics},
  volume={35},
  number={6},
  pages={1668--1676},
  year={2015},
  publisher={Radiological Society of North America}
}

@article{CQC2018,
  author={CQC},
  title={{Radiology review: a national review of radiology reporting within the NHS in England}},
  journal={Care Quality Commission},
  publisher = {Care Quality Commission},
  year={2018}
}


@article{KiM2014,
author = {Kim, Young W. and Mansfield, Liem T.},
title = {Fool Me Twice: Delayed Diagnoses in Radiology With Emphasis on Perpetuated Errors},
journal = {American Journal of Roentgenology},
volume = {202},
number = {3},
pages = {465-470},
year = {2014},
doi = {10.2214/AJR.13.11493},
note ={PMID: 24555582},
eprint = {https://doi.org/10.2214/AJR.13.11493}
}


@ARTICLE{HBC2020,
    author={Hashir, M. and Bertrand, H. and Cohen, J.P.},
    title={{Quantifying the Value of Lateral Views in Deep Learning for Chest X-rays}},
    journal={Medical Imaging with Deep Learning},
    year={2020},
    pages={288-303},
    source={Scopus},
}


@article{GeM2018,
  title={Added value of double reading in diagnostic radiology, a systematic review},
  author={Geijer, H{\aa}kan and Geijer, Mats},
  journal={Insights into imaging},
  volume={9},
  number={3},
  pages={287--301},
  year={2018},
  publisher={SpringerOpen}
}


@article{ZBL2018,
    doi = {10.1371/journal.pmed.1002683},
    author = {Zech, John R. AND Badgeley, Marcus A. AND Liu, Manway AND Costa, Anthony B. AND Titano, Joseph J. AND Oermann, Eric Karl},
    journal = {PLOS Medicine},
    publisher = {Public Library of Science},
    title = {Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: A cross-sectional study},
    year = {2018},
    month = {11},
    volume = {15},
    pages = {1-17},
    number = {11}
}


@inproceedings{CHB2020,
  title={{On the limits of cross-domain generalization in automated X-ray prediction}},
  author={Cohen, Joseph Paul and Hashir, Mohammad and Brooks, Rupert and Bertrand, Hadrien},
  booktitle={Medical Imaging with Deep Learning},
  pages={136--155},
  year={2020},
  organization={PMLR}
}


@article{WWG2020,
  title={Comparison of chest radiograph interpretations by artificial intelligence algorithm vs radiology residents},
  author={Wu, Joy T and Wong, Ken CL and Gur, Yaniv and Ansari, Nadeem and Karargyris, Alexandros and Sharma, Arjun and Morris, Michael and Saboury, Babak and Ahmad, Hassan and Boyko, Orest and others},
  journal={JAMA network open},
  volume={3},
  number={10},
  pages={e2022779--e2022779},
  year={2020},
  publisher={American Medical Association}
}


@article{XWB2019,
author = {Xu, Shuaijing and Wu, Hao and Bie, Rongfang},
doi = {10.1109/ACCESS.2018.2885997},
issn = {21693536},
journal = {IEEE Access},
keywords = {Chest X-Rays image,anomaly detection,deep neural network,self-Adapting loss function},
pages = {4466--4477},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
title = {{CXNet-m1: Anomaly Detection on Chest X-Rays with Image-Based Deep Learning}},
volume = {7},
year = {2019}
}



@article{BNG2019,
arxivId = {1803.02315},
author = {Baltruschat, Ivo M. and Nickisch, Hannes and Grass, Michael and Knopp, Tobias and Saalbach, Axel},
doi = {10.1038/s41598-019-42294-8},
eprint = {1803.02315},
issn = {20452322},
journal = {Scientific Reports},
pmid = {31011155},
title = {{Comparison of Deep Learning Approaches for Multi-Label Chest X-Ray Classification}},
year = {2019}
}


@article{CBF2019,
archivePrefix = {arXiv},
arxivId = {1901.11210},
author = {Cohen, Joseph Paul and Bertin, Paul and Frappier, Vincent},
eprint = {1901.11210},
month = jan,
journal = {arXiv:1901.11210},
title = {{Chester: A Web Delivered Locally Computed Chest X-Ray Disease Prediction System}},
year = {2019}
}



@article{WJL2020,
author = {Wang, Hongyu and Jia, Haozhe and Lu, Le and Xia, Yong},
doi = {10.1109/JBHI.2019.2928369},
issn = {21682208},
journal = {IEEE Journal of Biomedical and Health Informatics},
keywords = {Thorax disease classification,attention mechanism,deep learning,weakly supervised learning},
month = feb,
number = {2},
pages = {475--485},
pmid = {31329567},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
title = {{Thorax-Net: An Attention Regularized Deep Neural Network for Classification of Thoracic Diseases on Chest Radiography}},
volume = {24},
year = {2020}
}


% PLCO
@article{ACG2012,
    author = {Andriole, Gerald L. and Crawford, E. David and Grubb, Robert L., III and Buys, Saundra S. and Chia, David and Church, Timothy R. and Fouad, Mona N. and Isaacs, Claudine and Kvale, Paul A. and Reding, Douglas J. and Weissfeld, Joel L. and Yokochi, Lance A. and O’Brien, Barbara and Ragard, Lawrence R. and Clapp, Jonathan D. and Rathmell, Joshua M. and Riley, Thomas L. and Hsing, Ann W. and Izmirlian, Grant and Pinsky, Paul F. and Kramer, Barnett S. and Miller, Anthony B. and Gohagan, John K. and Prorok, Philip C. and for the PLCO Project Team},
    title = {Prostate Cancer Screening in the Randomized Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial: Mortality Results after 13 Years of Follow-up},
    journal = {JNCI: Journal of the National Cancer Institute},
    volume = {104},
    number = {2},
    pages = {125-132},
    year = {2012},
    month = {01},
    issn = {0027-8874},
    doi = {10.1093/jnci/djr500},
    eprint = {https://academic.oup.com/jnci/article-pdf/104/2/125/7674785/djr500.pdf},
}


@BOOK{WHO2016b,
	author = {WHO},
	title = {Chest radiography in tuberculosis detection: summary of current WHO recommendations and guidance on programmatic approaches},
	year = {2016},
	pages = {39 p.},
	publisher = {World Health Organization},
	type = {Publications}
}


@article{RRH2020,
author = {Rubin, Geoffrey D. and Ryerson, Christopher J. and Haramati, Linda B. and Sverzellati, Nicola and Kanne, Jeffrey P. and Raoof, Suhail and Schluger, Neil W. and Volpi, Annalisa and Yim, Jae-Joon and Martin, Ian B. K. and Anderson, Deverick J. and Kong, Christina and Altes, Talissa and Bush, Andrew and Desai, Sujal R. and Goldin, onathan and Goo, Jin Mo and Humbert, Marc and Inoue, Yoshikazu and Kauczor, Hans-Ulrich and Luo, Fengming and Mazzone, Peter J. and Prokop, Mathias and Remy-Jardin, Martine and Richeldi, Luca and Schaefer-Prokop, Cornelia M. and Tomiyama, Noriyuki and Wells, Athol U. and Leung, Ann N.},
title = {The Role of Chest Imaging in Patient Management during the COVID-19 Pandemic: A Multinational Consensus Statement from the Fleischner Society},
journal = {Radiology},
volume = {296},
number = {1},
pages = {172-180},
year = {2020},
doi = {10.1148/radiol.2020201365},
note ={PMID: 32255413},
eprint = {https://doi.org/10.1148/radiol.2020201365}
}


@techreport{PHS2021,
  title={{Unlocking Solutions in Imaging: working together to learn from failings in the NHS}},
  author={Parliamentary and Health Service Ombudsman},
  year={2021},
  month=jul,
  day={8},
  URL={https://www.ombudsman.org.uk/publications/unlocking-solutions-imaging-working-together-learn-failings-nhs},
  institution = {Parliamentary and Health Service Ombudsman}
}


%Age related conditions ref
@inproceedings{Rojas2015,
   title = {Aging and lung disease: Clinical impact and cellular and molecular pathways},
   author = {Mauricio Rojas and Ana L. Mora and Maria Kapetanaki and Nathaniel Weathington and Mark Gladwin and Oliver Eickelberg},
   doi = {10.1513/AnnalsATS.201508-484PL},
   issn = {23256621},
   issue = {12},
   journal = {Annals of the American Thoracic Society},
   keywords = {Aging,Lung disease,Lung repair},
   month = {12},
   pages = {S222-S227},
   pmid = {26653202},
   publisher = {American Thoracic Society},
   volume = {12},
   year = {2015},
}

@inproceedings{JPC2022,
  title={TorchXRayVision: A library of chest X-ray datasets and models},
  author={Cohen, Joseph Paul and Viviano, Joseph D and Bertin, Paul and Morrison, Paul and Torabian, Parsa and Guarrera, Matteo and Lungren, Matthew P and Chaudhari, Akshay and Brooks, Rupert and Hashir, Mohammad and others},
  booktitle={International Conference on Medical Imaging with Deep Learning},
  pages={231--249},
  year={2022},
  organization={PMLR}
}

@article{sellergren2022,
  title={Simplified transfer learning for chest radiography models using less data},
  author={Sellergren, Andrew B and Chen, Christina and Nabulsi, Zaid and Li, Yuanzhen and Maschinot, Aaron and Sarna, Aaron and Huang, Jenny and Lau, Charles and Kalidindi, Sreenivasa Raju and Etemadi, Mozziyar and others},
  journal={Radiology},
  volume={305},
  number={2},
  pages={454--465},
  year={2022},
  publisher={Radiological Society of North America}
}

@article{Bustos2020,
   abstract = {We present a labeled large-scale, high resolution chest x-ray dataset for the automated exploration of medical images along with their associated reports. This dataset includes more than 160,000 images obtained from 67,000 patients that were interpreted and reported by radiologists at San Juan Hospital (Spain) from 2009 to 2017, covering six different position views and additional information on image acquisition and patient demography. The reports were labeled with 174 different radiographic findings, 19 differential diagnoses and 104 anatomic locations organized as a hierarchical taxonomy and mapped onto standard Unified Medical Language System (UMLS) terminology. Of these reports, 27\% were manually annotated by trained physicians and the remaining set was labeled using a supervised method based on a recurrent neural network with attention mechanisms. The labels generated were then validated in an independent test set achieving a 0.93 Micro-F1 score. To the best of our knowledge, this is one of the largest public chest x-ray databases suitable for training supervised models concerning radiographs, and the first to contain radiographic reports in Spanish. The PadChest dataset can be downloaded from http://bimcv.cipf.es/bimcv-projects/padchest/.},
   author = {Aurelia Bustos and Antonio Pertusa and Jose Maria Salinas and Maria de la Iglesia-Vayá},
   doi = {10.1016/j.media.2020.101797},
   issn = {13618423},
   journal = {Medical Image Analysis},
   keywords = {Anatomical locations,Deep neural networks,Differential diagnoses,Radiographic findings,X-Ray image dataset},
   month = {12},
   pmid = {32877839},
   publisher = {Elsevier B.V.},
   title = {PadChest: A large chest x-ray image dataset with multi-label annotated reports},
   volume = {66},
   year = {2020},
}

@article{Wu2020,
   abstract = {Importance: Chest radiography is the most common diagnostic imaging examination performed in emergency departments (EDs). Augmenting clinicians with automated preliminary read assistants could help expedite their workflows, improve accuracy, and reduce the cost of care. Objective: To assess the performance of artificial intelligence (AI) algorithms in realistic radiology workflows by performing an objective comparative evaluation of the preliminary reads of anteroposterior (AP) frontal chest radiographs performed by an AI algorithm and radiology residents. Design, Setting, and Participants: This diagnostic study included a set of 72 findings assembled by clinical experts to constitute a full-fledged preliminary read of AP frontal chest radiographs. A novel deep learning architecture was designed for an AI algorithm to estimate the findings per image. The AI algorithm was trained using a multihospital training data set of 342126 frontal chest radiographs captured in ED and urgent care settings. The training data were labeled from their associated reports. Image-based F1 score was chosen to optimize the operating point on the receiver operating characteristics (ROC) curve so as to minimize the number of missed findings and overcalls per image read. The performance of the model was compared with that of 5 radiology residents recruited from multiple institutions in the US in an objective study in which a separate data set of 1998 AP frontal chest radiographs was drawn from a hospital source representative of realistic preliminary reads in inpatient and ED settings. A triple consensus with adjudication process was used to derive the ground truth labels for the study data set. The performance of AI algorithm and radiology residents was assessed by comparing their reads with ground truth findings. All studies were conducted through a web-based clinical study application system. The triple consensus data set was collected between February and October 2018. The comparison study was preformed between January and October 2019. Data were analyzed from October to February 2020. After the first round of reviews, further analysis of the data was performed from March to July 2020. Main Outcomes and Measures: The learning performance of the AI algorithm was judged using the conventional ROC curve and the area under the curve (AUC) during training and field testing on the study data set. For the AI algorithm and radiology residents, the individual finding label performance was measured using the conventional measures of label-based sensitivity, specificity, and positive predictive value (PPV). In addition, the agreement with the ground truth on the assignment of findings to images was measured using the pooled κ statistic. The preliminary read performance was recorded for AI algorithm and radiology residents using new measures of mean image-based sensitivity, specificity, and PPV designed for recording the fraction of misses and overcalls on a per image basis. The 1-sided analysis of variance test was used to compare the means of each group (AI algorithm vs radiology residents) using the F distribution, and the null hypothesis was that the groups would have similar means. Results: The trained AI algorithm achieved a mean AUC across labels of 0.807 (weighted mean AUC, 0.841) after training. On the study data set, which had a different prevalence distribution, the mean AUC achieved was 0.772 (weighted mean AUC, 0.865). The interrater agreement with ground truth finding labels for AI algorithm predictions had pooled κ value of 0.544, and the pooled κ for radiology residents was 0.585. For the preliminary read performance, the analysis of variance test was used to compare the distributions of AI algorithm and radiology residents' mean image-based sensitivity, PPV, and specificity. The mean image-based sensitivity for AI algorithm was 0.716 (95},
   author = {Joy T. Wu and Ken C.L. Wong and Yaniv Gur and Nadeem Ansari and Alexandros Karargyris and Arjun Sharma and Michael Morris and Babak Saboury and Hassan Ahmad and Orest Boyko and Ali Syed and Ashutosh Jadhav and Hongzhi Wang and Anup Pillai and Satyananda Kashyap and Mehdi Moradi and Tanveer Syeda-Mahmood},
   doi = {10.1001/jamanetworkopen.2020.22779},
   issn = {25743805},
   issue = {10},
   journal = {JAMA Network Open},
   month = {10},
   pmid = {33034642},
   publisher = {American Medical Association},
   title = {Comparison of Chest Radiograph Interpretations by Artificial Intelligence Algorithm vs Radiology Residents},
   volume = {3},
   year = {2020},
}

@article{IRK2019,
   abstract = {We develop an algorithm that can detect pneumonia from chest X-rays at a level exceeding practicing radiologists. Our algorithm, CheXNet, is a 121-layer convolutional neural network trained on ChestX-ray14, currently the largest publicly available chest X-ray dataset, containing over 100,000 frontal-view X-ray images with 14 diseases. Four practicing academic radiologists annotate a test set, on which we compare the performance of CheXNet to that of radiologists. We find that CheXNet exceeds average radiologist performance on the F1 metric. We extend CheXNet to detect all 14 diseases in ChestX-ray14 and achieve state of the art results on all 14 diseases.},
   author = {Pranav Rajpurkar and Jeremy Irvin and Kaylie Zhu and Brandon Yang and Hershel Mehta and Tony Duan and Daisy Ding and Aarti Bagul and Curtis Langlotz and Katie Shpanskaya and Matthew P. Lungren and Andrew Y. Ng},
   month = {11},
   title = {CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning},
   year = {2017},
   journal = {arXiv:1711.05225}
}

@article{AAW2020,
  title={COVID-CheXNet: hybrid deep learning framework for identifying COVID-19 virus in chest X-rays images},
  author={Al-Waisy, Alaa S and Al-Fahdawi, Shumoos and Mohammed, Mazin Abed and Abdulkareem, Karrar Hameed and Mostafa, Salama A and Maashi, Mashael S and Arif, Muhammad and Garcia-Zapirain, Begonya},
  journal={Soft computing},
  volume={27},
  number={5},
  pages={2657--2672},
  year={2023},
  publisher={Springer}
}


@article{nasrullah2019,
    author = {Nasrullah, N and Sang, J and Alam, MS and Mateen, M and Cai, B and Hu, H},
    title = {Automated Lung Nodule Detection and Classification Using Deep Learning Combined with Multiple Strategies},
    journal = {Sensors (Basel)},
    volume = {19},
    number = {17},
    pages = {3722},
    year = {2019},
    month = aug,
    doi = {10.3390/s19173722},
    pmid = {31466261},
    pmcid = {PMC6749467}
}

@article{Qin2019,
   abstract = {Deep learning (DL) neural networks have only recently been employed to interpret chest radiography (CXR) to screen and triage people for pulmonary tuberculosis (TB). No published studies have compared multiple DL systems and populations. We conducted a retrospective evaluation of three DL systems (CAD4TB, Lunit INSIGHT, and qXR) for detecting TB-associated abnormalities in chest radiographs from outpatients in Nepal and Cameroon. All 1196 individuals received a Xpert MTB/RIF assay and a CXR read by two groups of radiologists and the DL systems. Xpert was used as the reference standard. The area under the curve of the three systems was similar: Lunit (0.94, 95},
   author = {Zhi Zhen Qin and Melissa S. Sander and Bishwa Rai and Collins N. Titahong and Santat Sudrungrot and Sylvain N. Laah and Lal Mani Adhikari and E. Jane Carter and Lekha Puri and Andrew J. Codlin and Jacob Creswell},
   doi = {10.1038/s41598-019-51503-3},
   issn = {20452322},
   issue = {1},
   journal = {Scientific Reports},
   month = {12},
   pmid = {31628424},
   publisher = {Nature Publishing Group},
   title = {Using artificial intelligence to read chest radiographs for tuberculosis detection: A multi-site evaluation of the diagnostic accuracy of three deep learning systems},
   volume = {9},
   year = {2019},
}
@article{Hwang2020,
    author = {Hwang, EJ and Hong, JH and Lee, KH and Kim, JI and Nam, JG and Kim, DS and Choi, H and Yoo, SJ and Goo, JM and Park, CM},
    title = {Deep learning algorithm for surveillance of pneumothorax after lung biopsy: a multicenter diagnostic cohort study},
    journal = {Eur Radiol},
    volume = {30},
    number = {7},
    pages = {3660--3671},
    year = {2020},
    month = jul,
    doi = {10.1007/s00330-020-06771-3},
    pmid = {32162001},
    note = {Epub 2020 Mar 11}
}

@article{Hwang2019,
   abstract = {Importance: Interpretation of chest radiographs is a challenging task prone to errors, requiring expert readers. An automated system that can accurately classify chest radiographs may help streamline the clinical workflow. Objectives: To develop a deep learning-based algorithm that can classify normal and abnormal results from chest radiographs with major thoracic diseases including pulmonary malignant neoplasm, active tuberculosis, pneumonia, and pneumothorax and to validate the algorithm's performance using independent data sets. Design, Setting, and Participants: This diagnostic study developed a deep learning-based algorithm using single-center data collected between November 1, 2016, and January 31, 2017. The algorithm was externally validated with multicenter data collected between May 1 and July 31, 2018. A total of 54 221 chest radiographs with normal findings from 47 917 individuals (21 556 men and 26 361 women; mean [SD] age, 51 [16] years) and 35 613 chest radiographs with abnormal findings from 14 102 individuals (8373 men and 5729 women; mean [SD] age, 62 [15] years) were used to develop the algorithm. A total of 486 chest radiographs with normal results and 529 with abnormal results (1 from each participant; 628 men and 387 women; mean [SD] age, 53 [18] years) from 5 institutions were used for external validation. Fifteen physicians, including nonradiology physicians, board-certified radiologists, and thoracic radiologists, participated in observer performance testing. Data were analyzed in August 2018. Exposures: Deep learning-based algorithm. Main Outcomes and Measures: Image-wise classification performances measured by area under the receiver operating characteristic curve; lesion-wise localization performances measured by area under the alternative free-response receiver operating characteristic curve. Results: The algorithm demonstrated a median (range) area under the curve of 0.979 (0.973-1.000) for image-wise classification and 0.972 (0.923-0.985) for lesion-wise localization; the algorithm demonstrated significantly higher performance than all 3 physician groups in both image-wise classification (0.983 vs 0.814-0.932; all P < .005) and lesion-wise localization (0.985 vs 0.781-0.907; all P < .001). Significant improvements in both image-wise classification (0.814-0.932 to 0.904-0.958; all P < .005) and lesion-wise localization (0.781-0.907 to 0.873-0.938; all P < .001) were observed in all 3 physician groups with assistance of the algorithm. Conclusions and Relevance: The algorithm consistently outperformed physicians, including thoracic radiologists, in the discrimination of chest radiographs with major thoracic diseases, demonstrating its potential to improve the quality and efficiency of clinical practice.},
   author = {Eui Jin Hwang and Sunggyun Park and Kwang Nam Jin and Jung Im Kim and So Young Choi and Jong Hyuk Lee and Jin Mo Goo and Jaehong Aum and Jae Joon Yim and Julien G. Cohen and Gilbert R. Ferretti and Chang Min Park},
   doi = {10.1001/jamanetworkopen.2019.1095},
   issn = {25743805},
   issue = {3},
   journal = {JAMA network open},
   month = {3},
   pages = {e191095},
   pmid = {30901052},
   publisher = {NLM (Medline)},
   title = {Development and Validation of a Deep Learning-Based Automated Detection Algorithm for Major Thoracic Diseases on Chest Radiographs},
   volume = {2},
   year = {2019},
}


@article{shih2019augmenting,
  title={Augmenting the national institutes of health chest radiograph dataset with expert annotations of possible pneumonia},
  author={Shih, George and Wu, Carol C and Halabi, Safwan S and Kohli, Marc D and Prevedello, Luciano M and Cook, Tessa S and Sharma, Arjun and Amorosa, Judith K and Arteaga, Veronica and Galperin-Aizenberg, Maya and others},
  journal={Radiology: Artificial Intelligence},
  volume={1},
  number={1},
  pages={e180041},
  year={2019},
  publisher={Radiological Society of North America}
}


@article{Hochhegger2012,
    abstract = {Article in Jornal brasileiro de pneumologia: publicacao oficial da Sociedade Brasileira de Pneumologia e Tisilogia · October 2012 Source: PubMed CITATIONS 15 READS 1,932 7 authors, including: Some of the authors of this publication are also working on these related projects: Thoracic MRI View project Clubbed fingers: Radiological evaluation of the nail bed thickness},
    author = {Bruno Hochhegger and Gláucia Zanetti and José Moreira},
    issue = {5},
    journal = {J Bras Pneumol},
    pages = {656-665},
    title = {The chest and aging: radiological findings},
    volume = {38},
    year = {2012},
 }

@article{Mittelmark1993,
    author = {Maurice B Mittelmark and Bruce M Psaty and Pentti M Rautaharju and Linda P Fried and Nemat O Borhani and Russell P Tracy and Julius M Gardin and Daniel H O'leary},
    issue = {3},
    journal = {American Journal of Epidemiology},
    title = {Prevalence of Cardiovascular Diseases among Older Adults The Cardiovascular Health Study},
    volume = {137},
    year = {1993},
}

@article{Gross1985,
 author = {BH Gross and KF Gerke and KK Shirazi and WM Whitehouse and FL Bookstein},
 title = {Estimation of patient age based on plain chest radiographs},
 journal = {J Can Assoc Radiol.},
 year = {1985},
 month = {6},
 pages = {141-3},
 }

@Article{Dallora2019,
 author="Dallora, Ana Luiza
 and Berglund, Johan Sanmartin
 and Brogren, Martin
 and Kvist, Ola
 and Diaz Ruiz, Sandra
 and D{\"u}bbel, Andr{\'e}
 and Anderberg, Peter",
 title="Age Assessment of Youth and Young Adults Using Magnetic Resonance Imaging of the Knee: A Deep Learning Approach",
 journal="JMIR Med Inform",
 year="2019",
 month="Dec",
 day="5",
 volume="7",
 number="4",
 pages="e16291",
 keywords="age assessment; bone age; skeletal maturity; deep learning; convolutional neural networks; transfer learning; machine learning; magnetic resonance imaging; medical imaging; knee",
 abstract="Background: Bone age assessment (BAA) is an important tool for diagnosis and in determining the time of treatment in a number of pediatric clinical scenarios, as well as in legal settings where it is used to estimate the chronological age of an individual where valid documents are lacking. Traditional methods for BAA suffer from drawbacks, such as exposing juveniles to radiation, intra- and interrater variability, and the time spent on the assessment. The employment of automated methods such as deep learning and the use of magnetic resonance imaging (MRI) can address these drawbacks and improve the assessment of age. Objective: The aim of this paper is to propose an automated approach for age assessment of youth and young adults in the age range when the length growth ceases and growth zones are closed (14-21 years of age) by employing deep learning using MRI of the knee. Methods: This study carried out MRI examinations of the knee of 402 volunteer subjects---221 males (55.0{\%}) and 181 (45.0{\%}) females---aged 14-21 years. The method comprised two convolutional neural network (CNN) models: the first one selected the most informative images of an MRI sequence, concerning age-assessment purposes; these were then used in the second module, which was responsible for the age estimation. Different CNN architectures were tested, both training from scratch and employing transfer learning. Results: The CNN architecture that provided the best results was GoogLeNet pretrained on the ImageNet database. The proposed method was able to assess the age of male subjects in the range of 14-20.5 years, with a mean absolute error (MAE) of 0.793 years, and of female subjects in the range of 14-19.5 years, with an MAE of 0.988 years. Regarding the classification of minors---with the threshold of 18 years of age---an accuracy of 98.1{\%} for male subjects and 95.0{\%} for female subjects was achieved. Conclusions: The proposed method was able to assess the age of youth and young adults from 14 to 20.5 years of age for male subjects and 14 to 19.5 years of age for female subjects in a fully automated manner, without the use of ionizing radiation, addressing the drawbacks of traditional methods. ",
 issn="2291-9694",
 doi="10.2196/16291",
 }

 @article{Ieki2022,
    author = {Hirotaka Ieki and Kaoru Ito and Mike Saji and Rei Kawakami and Yuji Nagatomo and Kaori Takada and Toshiya Kariyasu and Haruhiko Machida and Satoshi Koyama and Hiroki Yoshida and Ryo Kurosawa and Hiroshi Matsunaga and Kazuo Miyazawa and Kouichi Ozaki and Yoshihiro Onouchi and Susumu Katsushika and Ryo Matsuoka and Hiroki Shinohara and Toshihiro Yamaguchi and Satoshi Kodera and Yasutomi Higashikuni and Katsuhito Fujiu and Hiroshi Akazawa and Nobuo Iguchi and Mitsuaki Isobe and Tsutomu Yoshikawa and Issei Komuro},
    doi = {10.1038/s43856-022-00220-6},
    issn = {2730-664X},
    issue = {1},
    journal = {Communications Medicine},
    month = {12},
    pages = {159},
    title = {Deep learning-based age estimation from chest X-rays indicates cardiovascular prognosis},
    volume = {2},
    year = {2022},
 }

@InProceedings{Oran2021,
    author    = {Lang, Oran and Gandelsman, Yossi and Yarom, Michal and Wald, Yoav and Elidan, Gal and Hassidim, Avinatan and Freeman, William T. and Isola, Phillip and Globerson, Amir and Irani, Michal and Mosseri, Inbar},
    title     = {Explaining in Style: Training a GAN To Explain a Classifier in StyleSpace},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {693-702}
}

@inproceedings{
Singla2020,
title={Explanation  by Progressive  Exaggeration},
author={Sumedha Singla and Brian Pollack and Junxiang Chen and Kayhan Batmanghelich},
booktitle={International Conference on Learning Representations},
year={2020},
}





@report{Antipov2017,
   abstract = {It has been recently shown that Generative Adversarial Networks (GANs) can produce synthetic images of exceptional visual fidelity. In this work, we propose the first GAN-based method for automatic face aging. Contrary to previous works employing GANs for altering of facial attributes, we make a particular emphasize on preserving the original person's identity in the aged version of his/her face. To this end, we introduce a novel approach for "Identity-Preserving" optimization of GAN's latent vectors. The objective evaluation of the resulting aged and rejuvenated face images by the state-of-the-art face recognition and age estimation solutions demonstrate the high potential of the proposed method.},
   author = {Grigory Antipov and Moez Baccouche and Jean-Luc Dugelay},
   keywords = {Deep Learning,Face Synthesis,GAN,Index Terms-Face Aging},
   title = {FACE AGING WITH CONDITIONAL GENERATIVE ADVERSARIAL NETWORKS},
}
@article{Sendik2019,
   abstract = {The estimation of a person's age based on a face image is a common biometric task conducted effortlessly by human observers. We present a dual Convolutional Neural Network (CNN) and Support Vector Regression (SVR) approach for face-based age estimation. A CNN is trained for representation learning, followed by Metric Learning, after which SVR is applied to the learned features. This allows to overcome the lack of large datasets with age annotations, by initially training the CNN for face recognition. The proposed scheme was applied to the MORPH-II and FG-Net datasets and compares favorably with contemporary state-of-the-art approaches. In particular, we show that domain adaptation which is essential for analyzing small-scale datasets, such as the FG-Net, can be achieved by retraining the SVR layer, rather than the CNN.},
   author = {Omry Sendik and Yosi Keller},
   doi = {10.1016/j.image.2019.08.003},
   issn = {09235965},
   journal = {Signal Processing: Image Communication},
   month = {10},
   pages = {368-375},
   publisher = {Elsevier B.V.},
   title = {DeepAge: Deep Learning of face-based age estimation},
   volume = {78},
   year = {2019},
}
@article{Despois2020,
   abstract = {Existing approaches and datasets for face aging produce results skewed towards the mean, with individual variations and expression wrinkles often invisible or overlooked in favor of global patterns such as the fattening of the face. Moreover, they offer little to no control over the way the faces are aged and can difficultly be scaled to large images, thus preventing their usage in many real-world applications. To address these limitations, we present an approach to change the appearance of a high-resolution image using ethnicity-specific aging information and weak spatial supervision to guide the aging process. We demonstrate the advantage of our proposed method in terms of quality, control, and how it can be used on high-definition images while limiting the computational overhead.},
   author = {Julien Despois and Frederic Flament and Matthieu Perrot},
   doi = {10.1007/978-3-030-67070-2_37},
   month = {8},
   title = {AgingMapGAN (AMGAN): High-Resolution Controllable Face Aging with Spatially-Aware Conditional GANs},
   year = {2020},
}
@article{Huang2021,
   abstract = {Face aging is to render a given face to predict its future appearance, which plays an important role in the information forensics and security field as the appearance of the face typically varies with age. Although impressive results have been achieved with conditional generative adversarial networks (cGANs), the existing cGANs-based methods typically use a single network to learn various aging effects between any two different age groups. However, they cannot simultaneously meet three essential requirements of face aging - including image quality, aging accuracy, and identity preservation - and usually generate aged faces with strong ghost artifacts when the age gap becomes large. Inspired by the fact that faces gradually age over time, this paper proposes a novel progressive face aging framework based on generative adversarial network (PFA-GAN) to mitigate these issues. Unlike the existing cGANs-based methods, the proposed framework contains several sub-networks to mimic the face aging process from young to old, each of which only learns some specific aging effects between two adjacent age groups. The proposed framework can be trained in an end-to-end manner to eliminate accumulative artifacts and blurriness. Moreover, this paper introduces an age estimation loss to take into account the age distribution for an improved aging accuracy, and proposes to use the Pearson correlation coefficient as an evaluation metric measuring the aging smoothness for face aging methods. Extensively experimental results demonstrate superior performance over existing (c)GANs-based methods, including the state-of-the-art one; e.g., PFA-GAN reduces the aging estimation errors by 0.23 and 0.35 and increases the identity preservation rates by 0.49 and 0.63 on two benchmarked datasets compared to the second best method for the challenging face aging from 30- to 51+. The source code is available at https://github.com/Hzzone/PFA-GAN.},
   author = {Zhizhong Huang and Shouzhen Chen and Junping Zhang and Hongming Shan},
   doi = {10.1109/TIFS.2020.3047753},
   issn = {15566021},
   journal = {IEEE Transactions on Information Forensics and Security},
   keywords = {Face aging,divide-and-conquer,generative adversarial networks,image translation,progressive neural networks},
   pages = {2031-2045},
   publisher = {Institute of Electrical and Electronics Engineers Inc.},
   title = {PFA-GAN: Progressive Face Aging with Generative Adversarial Network},
   volume = {16},
   year = {2021},
}


@article{Blanco21,
   author = {Rubén Fernández Blanco and Pilar Rosado and Esteban Vegas and Ferran Reverter},
   doi = {10.1016/j.ibmed.2021.100040},
   issn = {26665212},
   journal = {Intelligence-Based Medicine},
   pages = {100040},
   publisher = {Elsevier BV},
   title = {Medical image editing in the latent space of Generative Adversarial Networks},
   volume = {5},
   year = {2021},
}
@article{Ren2021,
   abstract = { Radiologists and pathologists frequently make highly consequential perceptual decisions. For example, visually searching for a tumor and recognizing whether it is malignant can have a life-changing impact on a patient. Unfortunately, all human perceivers— even radiologists—have perceptual biases. Because human perceivers (medical doctors) will, for the foreseeable future, be the final judges of whether a tumor is malignant, understanding and mitigating human perceptual biases is important. While there has been research on perceptual biases in medical image perception tasks, the stimuli used for these studies were highly artificial and often critiqued. Realistic stimuli have not been used because it has not been possible to generate or control them for psychophysical experiments. Here, we propose to use Generative Adversarial Networks (GAN) to create vivid and realistic medical image stimuli that can be used in psychophysical and computer vision studies of medical image perception. Our model can generate tumor-like stimuli with specified shapes and realistic textures in a controlled manner. Various experiments showed the authenticity of our GAN-generated stimuli and the controllability of our model. },
   author = {Zhihang Ren and Stella X. Yu and David Whitney},
   doi = {10.2352/issn.2470-1173.2021.11.hvei-112},
   issn = {2470-1173},
   issue = {11},
   journal = {Electronic Imaging},
   month = {2},
   pages = {112-1-112-6},
   publisher = {Society for Imaging Science & Technology},
   title = {Controllable Medical Image Generation via Generative Adversarial Networks},
   volume = {2021},
   year = {2021},
}
@article{Fetty2020,
   abstract = {Introduction: This paper explores the potential of the StyleGAN model as an high-resolution image generator for synthetic medical images. The possibility to generate sample patient images of different modalities can be helpful for training deep learning algorithms as e.g. a data augmentation technique. Methods: The StyleGAN model was trained on Computed Tomography (CT) and T2- weighted Magnetic Resonance (MR) images from 100 patients with pelvic malignancies. The resulting model was investigated with regards to three features: Image Modality, Sex, and Longitudinal Slice Position. Further, the style transfer feature of the StyleGAN was used to move images between the modalities. The root-mean-squard error (RMSE) and the Mean Absolute Error (MAE) were used to quantify errors for MR and CT, respectively. Results: We demonstrate how these features can be transformed by manipulating the latent style vectors, and attempt to quantify how the errors change as we move through the latent style space. The best results were achieved by using the style transfer feature of the StyleGAN (58.7 HU MAE for MR to CT and 0.339 RMSE for CT to MR). Slices below and above an initial central slice can be predicted with an error below 75 HU MAE and 0.3 RMSE within 4 cm for CT and MR, respectively. Discussion: The StyleGAN is a promising model to use for generating synthetic medical images for MR and CT modalities as well as for 3D volumes.},
   author = {Lukas Fetty and Mikael Bylund and Peter Kuess and Gerd Heilemann and Tufve Nyholm and Dietmar Georg and Tommy Löfstedt},
   doi = {10.1016/j.zemedi.2020.05.001},
   issn = {18764436},
   issue = {4},
   journal = {Zeitschrift fur Medizinische Physik},
   keywords = {StyleGAN, Image synthesis, Latent space},
   month = {11},
   pages = {305-314},
   pmid = {32564924},
   publisher = {Elsevier GmbH},
   title = {Latent space manipulation for high-resolution medical image synthesis via the StyleGAN},
   volume = {30},
   year = {2020},
}

@article{Schutte21,
   abstract = {Privacy concerns around sharing personally identifiable information are a major barrier to data sharing in medical research. In many cases, researchers have no interest in a particular individual’s information but rather aim to derive insights at the level of cohorts. Here, we utilise generative adversarial networks (GANs) to create medical imaging datasets consisting entirely of synthetic patient data. The synthetic images ideally have, in aggregate, similar statistical properties to those of a source dataset but do not contain sensitive personal information. We assess the quality of synthetic data generated by two GAN models for chest radiographs with 14 radiology findings and brain computed tomography (CT) scans with six types of intracranial haemorrhages. We measure the synthetic image quality by the performance difference of predictive models trained on either the synthetic or the real dataset. We find that synthetic data performance disproportionately benefits from a reduced number of classes. Our benchmark also indicates that at low numbers of samples per class, label overfitting effects start to dominate GAN training. We conducted a reader study in which trained radiologists discriminate between synthetic and real images. In accordance with our benchmark results, the classification accuracy of radiologists improves with an increasing resolution. Our study offers valuable guidelines and outlines practical conditions under which insights derived from synthetic images are similar to those that would have been derived from real data. Our results indicate that synthetic data sharing may be an attractive alternative to sharing real patient-level data in the right setting.},
   author = {August DuMont Schütte and Jürgen Hetzel and Sergios Gatidis and Tobias Hepp and Benedikt Dietz and Stefan Bauer and Patrick Schwab},
   doi = {10.1038/s41746-021-00507-3},
   issn = {23986352},
   issue = {1},
   journal = {npj Digital Medicine},
   month = {12},
   publisher = {Nature Research},
   title = {Overcoming barriers to data sharing with medical image generation: a comprehensive evaluation},
   volume = {4},
   year = {2021},
}



@article{Rokicki2021,
   abstract = {The deviation between chronological age and age predicted using brain MRI is a putative marker of overall brain health. Age prediction based on structural MRI data shows high accuracy in common brain disorders. However, brain aging is complex and heterogenous, both in terms of individual differences and the underlying biological processes. Here, we implemented a multimodal model to estimate brain age using different combinations of cortical area, thickness and sub-cortical volumes, cortical and subcortical T1/T2-weighted ratios, and cerebral blood flow (CBF) based on arterial spin labeling. For each of the 11 models we assessed the age prediction accuracy in healthy controls (HC, n = 750) and compared the obtained brain age gaps (BAGs) between age-matched subsets of HC and patients with Alzheimer's disease (AD, n = 54), mild (MCI, n = 90) and subjective (SCI, n = 56) cognitive impairment, schizophrenia spectrum (SZ, n = 159) and bipolar disorder (BD, n = 135). We found highest age prediction accuracy in HC when integrating all modalities. Furthermore, two-group case–control classifications revealed highest accuracy for AD using global T1-weighted BAG, while MCI, SCI, BD and SZ showed strongest effects in CBF-based BAGs. Combining multiple MRI modalities improves brain age prediction and reveals distinct deviations in patients with psychiatric and neurological disorders. The multimodal BAG was most accurate in predicting age in HC, while group differences between patients and HC were often larger for BAGs based on single modalities. These findings indicate that multidimensional neuroimaging of patients may provide a brain-based mapping of overlapping and distinct pathophysiology in common disorders.},
   author = {Jaroslav Rokicki and Thomas Wolfers and Wibeke Nordhøy and Natalia Tesli and Daniel S. Quintana and Dag Alnæs and Genevieve Richard and Ann Marie G. de Lange and Martina J. Lund and Linn Norbom and Ingrid Agartz and Ingrid Melle and Terje Nærland and Geir Selbæk and Karin Persson and Jan Egil Nordvik and Emanuel Schwarz and Ole A. Andreassen and Tobias Kaufmann and Lars T. Westlye},
   doi = {10.1002/hbm.25323},
   issn = {10970193},
   issue = {6},
   journal = {Human Brain Mapping},
   keywords = {MRI,T1w/T2w ratio,arterial spin labeling,brain age,brain disorders,cerebral blood flow,machine learning,multimodal imaging},
   month = {4},
   pages = {1714-1726},
   pmid = {33340180},
   publisher = {John Wiley and Sons Inc},
   title = {Multimodal imaging improves brain age prediction and reveals distinct abnormalities in patients with psychiatric and neurological disorders},
   volume = {42},
   year = {2021},
}
@article{Peng2021,
   abstract = {Deep learning has huge potential for accurate disease prediction with neuroimaging data, but the prediction performance is often limited by training-dataset size and computing memory requirements. To address this, we propose a deep convolutional neural network model, Simple Fully Convolutional Network (SFCN), for accurate prediction of brain age using T1-weighted structural MRI data. Compared with other popular deep network architectures, SFCN has fewer parameters, so is more compatible with small dataset size and 3D volume data. The network architecture was combined with several techniques for boosting performance, including data augmentation, pre-training, model regularization, model ensemble and prediction bias correction. We compared our overall SFCN approach with several widely-used machine learning models. It achieved state-of-the-art performance in UK Biobank data (N = 14,503), with mean absolute error (MAE) = 2.14y in brain age prediction and 99.5},
   author = {Han Peng and Weikang Gong and Christian F. Beckmann and Andrea Vedaldi and Stephen M. Smith},
   doi = {10.1016/j.media.2020.101871},
   issn = {13618423},
   journal = {Medical Image Analysis},
   keywords = {Brain age prediction,Convolutional neuralnetworks,Neuroimaging,Predictive analysis},
   month = {2},
   pmid = {33197716},
   publisher = {Elsevier B.V.},
   title = {Accurate brain age prediction with lightweight deep neural networks},
   volume = {68},
   year = {2021},
}
@article{Cole2017,
   abstract = {Machine learning analysis of neuroimaging data can accurately predict chronological age in healthy people. Deviations from healthy brain ageing have been associated with cognitive impairment and disease. Here we sought to further establish the credentials of ‘brain-predicted age’ as a biomarker of individual differences in the brain ageing process, using a predictive modelling approach based on deep learning, and specifically convolutional neural networks (CNN), and applied to both pre-processed and raw T1-weighted MRI data. Firstly, we aimed to demonstrate the accuracy of CNN brain-predicted age using a large dataset of healthy adults (N = 2001). Next, we sought to establish the heritability of brain-predicted age using a sample of monozygotic and dizygotic female twins (N = 62). Thirdly, we examined the test-retest and multi-centre reliability of brain-predicted age using two samples (within-scanner N = 20; between-scanner N = 11). CNN brain-predicted ages were generated and compared to a Gaussian Process Regression (GPR) approach, on all datasets. Input data were grey matter (GM) or white matter (WM) volumetric maps generated by Statistical Parametric Mapping (SPM) or raw data. CNN accurately predicted chronological age using GM (correlation between brain-predicted age and chronological age r = 0.96, mean absolute error [MAE] = 4.16 years) and raw (r = 0.94, MAE = 4.65 years) data. This was comparable to GPR brain-predicted age using GM data (r = 0.95, MAE = 4.66 years). Brain-predicted age was a heritable phenotype for all models and input data (h2 ≥ 0.5). Brain-predicted age showed high test-retest reliability (intraclass correlation coefficient [ICC] = 0.90–0.99). Multi-centre reliability was more variable within high ICCs for GM (0.83–0.96) and poor-moderate levels for WM and raw data (0.51–0.77). Brain-predicted age represents an accurate, highly reliable and genetically-influenced phenotype, that has potential to be used as a biomarker of brain ageing. Moreover, age predictions can be accurately generated on raw T1-MRI data, substantially reducing computation time for novel data, bringing the process closer to giving real-time information on brain health in clinical settings.},
   author = {James H. Cole and Rudra P.K. Poudel and Dimosthenis Tsagkrasoulis and Matthan W.A. Caan and Claire Steves and Tim D. Spector and Giovanni Montana},
   doi = {10.1016/j.neuroimage.2017.07.059},
   issn = {10959572},
   journal = {NeuroImage},
   keywords = {Biomarker,Brain ageing,Convolutional neural networks,Deep learning,Gaussian processes,Heritability,Neuroimaging,Reliability},
   month = {12},
   pages = {115-124},
   pmid = {28765056},
   publisher = {Academic Press Inc.},
   title = {Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker},
   volume = {163},
   year = {2017},
}


@inbook{Santeramo2018,
title = "Longitudinal detection of radiological abnormalities with time-modulated lstm",
abstract = "Convolutional neural networks (CNNs) have been successfully employed in recent years for the detection of radiological abnormalities in medical images such as plain x-rays. To date, most studies use CNNs on individual examinations in isolation and discard previously available clinical information. In this study we set out to explore whether Long-Short-Term-Memory networks (LSTMs) can be used to improve classification performance when modelling the entire sequence of radiographs that may be available for a given patient, including their reports. A limitation of traditional LSTMs, though, is that they implicitly assume equally-spaced observations, whereas the radiological exams are event-based, and therefore irregularly sampled. Using both a simulated dataset and a large-scale chest x-ray dataset, we demonstrate that a simple modification of the LSTM architecture, which explicitly takes into account the time lag between consecutive observations, can boost classification performance. Our empirical results demonstrate improved detection of commonly reported abnormalities on chest x-rays such as cardiomegaly, consolidation, pleural effusion and hiatus hernia.",
keywords = "CNN, Deep learning, LSTM, Medical imaging, Time-modulated LSTM, X-rays",
author = "Ruggiero Santeramo and Samuel Withey and Giovanni Montana",
year = "2018",
month = jan,
day = "1",
doi = "10.1007/978-3-030-00889-5_37",
language = "English",
isbn = "9783030008888",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "326--333",
booktitle = "Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support - 4th International Workshop, DLMIA 2018 and 8th International Workshop, ML-CDS 2018 Held in Conjunction with MICCAI 2018",
address = "Germany",
note = "4th International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2018 and 8th International Workshop on Multimodal Learning for Clinical Decision Support, ML-CDS 2018 Held in Conjunction with MICCAI 2018 ; Conference date: 20-09-2018 Through 20-09-2018",
}


@article{Raghu2021,
title = {Deep Learning to Estimate Biological Age From Chest Radiographs},
journal = {JACC: Cardiovascular Imaging},
volume = {14},
number = {11},
pages = {2226-2236},
year = {2021},
issn = {1936-878X},
doi = {https://doi.org/10.1016/j.jcmg.2021.01.008},
author = {Vineet K. Raghu and Jakob Weiss and Udo Hoffmann and Hugo J.W.L. Aerts and Michael T. Lu},
keywords = {biological age, cardiovascular risk prediction, chest radiographs, deep learning},
abstract = {Objectives
The goal of this study was to assess whether a deep learning estimate of age from a chest radiograph image (CXR-Age) can predict longevity beyond chronological age.
Background
Chronological age is an imperfect measure of longevity. Biological age, a measure of overall health, may improve personalized care. This paper proposes a new way to estimate biological age using a convolutional neural network that takes as input a CXR image and outputs a chest x-ray age (in years) as a measure of long-term mortality risk.
Methods
Conclusions
Based on a CXR image, CXR-Age predicted long-term all-cause and cardiovascular mortality.}
}


@article{Selvaraju2017,
   abstract = {We propose a technique for producing 'visual explanations' for decisions from a large class of Convolutional Neural Network (CNN)-based models, making them more transparent. Our approach - Gradient-weighted Class Activation Mapping (Grad-CAM), uses the gradients of any target concept (say logits for 'dog' or even a caption), flowing into the final convolutional layer to produce a coarse localization map highlighting the important regions in the image for predicting the concept. Unlike previous approaches, Grad- CAM is applicable to a wide variety of CNN model-families: (1) CNNs with fully-connected layers (e.g. VGG), (2) CNNs used for structured outputs (e.g. captioning), (3) CNNs used in tasks with multi-modal inputs (e.g. visual question answering) or reinforcement learning, without architectural changes or re-training. We combine Grad-CAM with existing fine-grained visualizations to create a high-resolution class-discriminative visualization, Guided Grad-CAM, and apply it to image classification, image captioning, and visual question answering (VQA) models, including ResNet-based architectures. In the context of image classification models, our visualizations (a) lend insights into failure modes of these models (showing that seemingly unreasonable predictions have reasonable explanations), (b) outperform previous methods on the ILSVRC-15 weakly-supervised localization task, (c) are more faithful to the underlying model, and (d) help achieve model generalization by identifying dataset bias. For image captioning and VQA, our visualizations show even non-attention based models can localize inputs. Finally, we design and conduct human studies to measure if Grad-CAM explanations help users establish appropriate trust in predictions from deep networks and show that Grad-CAM helps untrained users successfully discern a 'stronger' deep network from a 'weaker' one even when both make identical predictions. Our code is available at https: //github.com/ramprs/grad-cam/ along with a demo on CloudCV [2] and video at youtu.be/COjUB9Izk6E.},
   author = {Ramprasaath R. Selvaraju and Michael Cogswell and Abhishek Das and Ramakrishna Vedantam and Devi Parikh and Dhruv Batra},
   doi = {10.1109/ICCV.2017.74},
   isbn = {9781538610329},
   issn = {15505499},
   journal = {Proceedings of the IEEE International Conference on Computer Vision},
   month = {12},
   pages = {618-626},
   publisher = {Institute of Electrical and Electronics Engineers Inc.},
   title = {Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization},
   volume = {2017-October},
   year = {2017},
}

@article{Irvin2019,
   abstract = {Large, labeled datasets have driven deep learning methods to achieve expert-level performance on a variety of medical imaging tasks. We present CheXpert, a large dataset that contains 224,316 chest radiographs of 65,240 patients. We design a labeler to automatically detect the presence of 14 observations in radiology reports, capturing uncertainties inherent in radiograph interpretation. We investigate different approaches to using the uncertainty labels for training convolu-tional neural networks that output the probability of these observations given the available frontal and lateral radiographs. On a validation set of 200 chest radiographic studies which were manually annotated by 3 board-certified radiologists, we find that different uncertainty approaches are useful for different pathologies. We then evaluate our best model on a test set composed of 500 chest radiographic studies annotated by a consensus of 5 board-certified radiologists, and compare the performance of our model to that of 3 additional radiologists in the detection of 5 selected pathologies. On Cardiomegaly, Edema, and Pleural Effusion, the model ROC and PR curves lie above all 3 radiologist operating points. We release the dataset to the public as a standard benchmark to evaluate performance of chest radiograph interpretation models. 1},
   author = {Jeremy Irvin and Andrew Y Ng et al.},
   pages = {19},
   title = {CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison},
}


@inproceedings{Karargyris2019,
  title={Age prediction using a large chest x-ray dataset},
  author={Karargyris, Alexandros and Kashyap, Satyananda and Wu, Joy T and Sharma, Arjun and Moradi, Mehdi and Syeda-Mahmood, Tanveer},
  booktitle={Medical Imaging 2019: Computer-Aided Diagnosis},
  volume={10950},
  pages={468--476},
  year={2019},
  organization={SPIE}
}


@article{Yang2021,
   abstract = {Background: The performance of chest radiography-based age and sex prediction has not been well validated. We used a deep learning model to predict the age and sex of healthy adults based on chest radiographs (CXRs). Methods: In this retrospective study, 66,643 CXRs of 47,060 healthy adults were used for model training and testing. In total, 47,060 individuals (mean age ± standard deviation, 38.7 ± 11.9 years; 22,144 males) were included. By using chronological ages as references, mean absolute error (MAE), root mean square error (RMSE), and Pearson’s correlation coefficient were used to assess the model performance. Summarized class activation maps were used to highlight the activated anatomical regions. The area under the curve (AUC) was used to examine the validity for sex prediction. Results: When model predictions were compared with the chronological ages, the MAE was 2.1 years, RMSE was 2.8 years, and Pearson’s correlation coefficient was 0.97 (p <0.001). Cervical, thoracic spines, first ribs, aortic arch, heart, rib cage, and soft tissue of thorax and flank seemed to be the most crucial activated regions in the age prediction model. The sex prediction model demonstrated an AUC of >0.99. Conclusion: Deep learning can accurately estimate age and sex based on CXRs.},
   author = {Chung Yi Yang and Yi Ju Pan and Yen Chou and Chia Jung Yang and Ching Chung Kao and Kuan Chieh Huang and Jing Shan Chang and Hung Chieh Chen and Kuei Hong Kuo},
   doi = {10.3390/jcm10194431},
   issn = {20770383},
   issue = {19},
   journal = {Journal of Clinical Medicine},
   keywords = {Age prediction,Chest radiograph,Deep learning,Sex prediction},
   month = {10},
   publisher = {MDPI},
   title = {Using deep neural networks for predicting age and sex in healthy adult chest radiographs},
   volume = {10},
   year = {2021},
}

@article{Ieki2021,
  title={Deep learning-based chest X-ray age serves as a novel biomarker for cardiovascular aging},
  author={Ieki, Hirotaka and Ito, Kaoru and Saji, Mike and Kawakami, Rei and Nagatomo, Yuji and Koyama, Satoshi and Matsunaga, Hiroshi and Miyazawa, Kazuo and Ozaki, Kouichi and Onouchi, Yoshihiro and others},
  journal={bioRxiv},
  pages={2021--03},
  year={2021},
  publisher={Cold Spring Harbor Laboratory}
}

@article{Sabottke2020,
  title={Estimation of age in unidentified patients via chest radiography using convolutional neural network regression},
  author={Sabottke, Carl F and Breaux, Marc A and Spieler, Bradley M},
  journal={Emergency radiology},
  volume={27},
  pages={463--468},
  year={2020},
  publisher={Springer}
}


@article{Shan2018,
   abstract = {Low-dose computed tomography (LDCT) has attracted major attention in the medical imaging field, since CT-associated X-ray radiation carries health risks for patients. The reduction of the CT radiation dose, however, compromises the signal-to-noise ratio, which affects image quality and diagnostic performance. Recently, deep-learning-based algorithms have achieved promising results in LDCT denoising, especially convolutional neural network (CNN) and generative adversarial network (GAN) architectures. This paper introduces a conveying path-based convolutional encoder-decoder (CPCE) network in 2-D and 3-D configurations within the GAN framework for LDCT denoising. A novel feature of this approach is that an initial 3-D CPCE denoising model can be directly obtained by extending a trained 2-D CNN, which is then fine-tuned to incorporate 3-D spatial information from adjacent slices. Based on the transfer learning from 2-D to 3-D, the 3-D network converges faster and achieves a better denoising performance when compared with a training from scratch. By comparing the CPCE network with recently published work based on the simulated Mayo data set and the real MGH data set, we demonstrate that the 3-D CPCE denoising model has a better performance in that it suppresses image noise and preserves subtle structures.},
   author = {Hongming Shan and Yi Zhang and Qingsong Yang and Uwe Kruger and Mannudeep K. Kalra and Ling Sun and Wenxiang Cong and Ge Wang},
   doi = {10.1109/TMI.2018.2832217},
   issn = {1558254X},
   issue = {6},
   journal = {IEEE Transactions on Medical Imaging},
   keywords = {3D spatial information,Low-dose CT,convolutional neural network,denoising,generative adversarial network},
   month = {6},
   pages = {1522-1534},
   pmid = {29870379},
   publisher = {Institute of Electrical and Electronics Engineers Inc.},
   title = {3-D Convolutional Encoder-Decoder Network for Low-Dose CT via Transfer Learning From a 2-D Trained Network},
   volume = {37},
   year = {2018},
}

@article{Tang2021,
   abstract = {The interpretation of medical images is a complex cognition procedure requiring cautious observation, precise understanding/parsing of the normal body anatomies, and combining knowledge of physiology and pathology. Interpreting chest X-ray (CXR) images is challenging since the 2D CXR images show the superimposition on internal organs/tissues with low resolution and poor boundaries. Unlike previous CXR computer-aided diagnosis works that focused on disease diagnosis/classification, we firstly propose a deep disentangled generative model (DGM) simultaneously generating abnormal disease residue maps and “radiorealistic” normal CXR images from an input abnormal CXR image. The intuition of our method is based on the assumption that disease regions usually superimpose upon or replace the pixels of normal tissues in an abnormal CXR. Thus, disease regions can be disentangled or decomposed from the abnormal CXR by comparing it with a generated patient-specific normal CXR. DGM consists of three encoder-decoder architecture branches: one for radiorealistic normal CXR image synthesis using adversarial learning, one for disease separation by generating a residue map to delineate the underlying abnormal region, and the other one for facilitating the training process and enhancing the model's robustness on noisy data. A self-reconstruction loss is adopted in the first two branches to enforce the generated normal CXR image to preserve similar visual structures as the original CXR. We evaluated our model on a large-scale chest X-ray dataset. The results show that our model can generate disease residue/saliency maps (coherent with radiologist annotations) along with radiorealistic and patient specific normal CXR images. The disease residue/saliency map can be used by radiologists to improve the CXR reading efficiency in clinical practice. The synthesized normal CXR can be used for data augmentation and normal control of personalized longitudinal disease study. Furthermore, DGM quantitatively boosts the diagnosis performance on several important clinical applications, including normal/abnormal CXR classification, and lung opacity classification/detection.},
   author = {Youbao Tang and Yuxing Tang and Yingying Zhu and Jing Xiao and Ronald M. Summers},
   doi = {10.1016/j.media.2020.101839},
   issn = {13618423},
   journal = {Medical Image Analysis},
   keywords = {Chest radiography (X-ray),Disease decomposition,Disentangled representation learning,Medical image synthesis},
   month = {1},
   pmid = {33080508},
   publisher = {Elsevier B.V.},
   title = {A disentangled generative model for disease decomposition in chest X-rays via normal image synthesis},
   volume = {67},
   year = {2021},
}

@article{Barratt2018,
   abstract = {Deep generative models are powerful tools that have produced impressive results in recent years. These advances have been for the most part empirically driven, making it essential that we use high quality evaluation metrics. In this paper, we provide new insights into the Inception Score, a recently proposed and widely used evaluation metric for generative models, and demonstrate that it fails to provide useful guidance when comparing models. We discuss both suboptimalities of the metric itself and issues with its application. Finally, we call for researchers to be more systematic and careful when evaluating and comparing generative models, as the advancement of the field depends upon it.},
   author = {Shane Barratt and Rishi Sharma},
   month = {1},
   title = {A Note on the Inception Score},
   year = {2018},
}
@article{Mukherjee2020,
   abstract = {Lung cancer is the most common fatal malignancy in adults worldwide, and non-small-cell lung cancer (NSCLC) accounts for 85},
   author = {Pritam Mukherjee and Mu Zhou and Edward Lee and Anne Schicht and Yoganand Balagurunathan and Sandy Napel and Robert Gillies and Simon Wong and Alexander Thieme and Ann Leung and Olivier Gevaert},
   doi = {10.1038/s42256-020-0173-6},
   issue = {5},
   journal = {Nature Machine Intelligence},
   month = {5},
   pages = {274-282},
   publisher = {Springer Science and Business Media LLC},
   title = {A shallow convolutional neural network predicts prognosis of lung cancer patients in multi-institutional computed tomography image datasets},
   volume = {2},
   year = {2020},
}
@article{Chen2020,
   abstract = {This paper presents SimCLR: a simple framework for contrastive learning of visual representations. We simplify recently proposed contrastive self-supervised learning algorithms without requiring specialized architectures or a memory bank. In order to understand what enables the contrastive prediction tasks to learn useful representations, we systematically study the major components of our framework. We show that (1) composition of data augmentations plays a critical role in defining effective predictive tasks, (2) introducing a learnable nonlinear transformation between the representation and the contrastive loss substantially improves the quality of the learned representations, and (3) contrastive learning benefits from larger batch sizes and more training steps compared to supervised learning. By combining these findings, we are able to considerably outperform previous methods for self-supervised and semi-supervised learning on ImageNet. A linear classifier trained on self-supervised representations learned by SimCLR achieves 76.5},
   author = {Ting Chen and Simon Kornblith and Mohammad Norouzi and Geoffrey Hinton},
   month = {2},
   title = {A Simple Framework for Contrastive Learning of Visual Representations},
   year = {2020},
}
@article{Ramesh2018,
   abstract = {A generative model with a disentangled representation allows for independent control over different aspects of the output. Learning disentangled representations has been a recent topic of great interest, but it remains poorly understood. We show that even for GANs that do not possess disentangled representations, one can find curved trajectories in latent space over which local disentanglement occurs. These trajectories are found by iteratively following the leading right-singular vectors of the Jacobian of the generator with respect to its input. Based on this insight, we describe an efficient regularizer that aligns these vectors with the coordinate axes, and show that it can be used to induce disentangled representations in GANs, in a completely unsupervised manner.},
   author = {Aditya Ramesh and Youngduck Choi and Yann LeCun},
   month = {12},
   title = {A Spectral Regularizer for Unsupervised Disentanglement},
   year = {2018},
}

@generic{Litjens2017,
   abstract = {Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks. Concise overviews are provided of studies per application area: neuro, retinal, pulmonary, digital pathology, breast, cardiac, abdominal, musculoskeletal. We end with a summary of the current state-of-the-art, a critical discussion of open challenges and directions for future research.},
   author = {Geert Litjens and Thijs Kooi and Babak Ehteshami Bejnordi and Arnaud Arindra Adiyoso Setio and Francesco Ciompi and Mohsen Ghafoorian and Jeroen A.W.M. van der Laak and Bram van Ginneken and Clara I. Sánchez},
   doi = {10.1016/j.media.2017.07.005},
   issn = {13618423},
   journal = {Medical Image Analysis},
   keywords = {Convolutional neural networks,Deep learning,Medical imaging,Survey},
   month = {12},
   pages = {60-88},
   pmid = {28778026},
   publisher = {Elsevier B.V.},
   title = {A survey on deep learning in medical image analysis},
   volume = {42},
   year = {2017},
}
@report{Hu2018,
   abstract = {We describe an adversarial learning approach to constrain convolu-tional neural network training for image registration, replacing heuristic smoothness measures of displacement fields often used in these tasks. Using minimally-invasive prostate cancer intervention as an example application, we demonstrate the feasibility of utilizing biomechanical simulations to regularize a weakly-supervised anatomical-label-driven registration network for aligning pre-procedural magnetic resonance (MR) and 3D intra-procedural transrectal ultrasound (TRUS) images. A discriminator network is optimized to distinguish the registration predicted displacement fields from the motion data simulated by finite element analysis. During training, the registration network simultaneously aims to maximize similarity between anatomical labels that drives image alignment and to minimize an adversarial generator loss that measures divergence between the predicted-and simulated deformation. The end-to-end trained network enables efficient and fully-automated registration that only requires an MR and TRUS image pair as input, without anatomical labels or simulated data during inference. 108 pairs of labelled MR and TRUS images from 76 prostate cancer patients and 71,500 nonlinear finite-element simulations from 143 different patients were used for this study. We show that, with only gland segmentation as training labels, the proposed method can help predict physically plausible deformation without any other smoothness penalty. Based on cross-validation experiments using 834 pairs of independent validation landmarks, the proposed adver-sarial-regularized registration achieved a target registration error of 6.3 mm that is significantly lower than those from several other regularization methods.},
   author = {Yipeng Hu and Eli Gibson and Nooshin Ghavami and Ester Bonmati and Caroline M Moore and Mark Emberton and Tom Vercauteren and J Alison Noble and Dean C Barratt},
   title = {Adversarial Deformation Regularization for Training Image Registration Neural Networks},
}

@article{Chen2019,
   abstract = {We present a new Adaptive Error Correction Net (AEC-Net) to formulate the estimation of Cobb anges from spinal X-rays as a high-precision regression task. Our AEC-Net introduces two novel innovations. (1) The AEC-Net contains two networks calculating landmarks and Cobb angles separately, which robustly solve the disadvantage of ambiguity in X-rays since these networks focus on more features. It effectively handles the nonlinear relationship between input images and quantitative outputs, while explicitly capturing the intrinsic features of input images. (2) Based on the two estimated angles, the AEC-Net proposed a new loss function to calculate the final Cobb angles. The optimization of the loss function is based on a high-precision calculation method. The deep learning structure is used to complete this optimization, which achieves higher accuracy and efficiency. We validate our method with the spinal X-rays dataset of 581 subjects with signs of scoliosis at varying extents. The proposed method achieves high accuracy and robustness on the Cobb angle estimations. Comparing to the exsiting conventional methods suffering from tremendous variability and low reliability caused by high ambiguity and variability around boundaries of the vertebrae, the AEC-Net obtain Cobb angles accurately and robustly, which indicates its great potential in clinical use. The highly accurate Cobb angles produced by our framework can be used by clinicians for comprehensive scoliosis assessment, and possibly be further extended to other clinical applications.},
   author = {Bo Chen and Qiuhao Xu and Liansheng Wang and Stephanie Leung and Jonathan Chung and Shuo Li},
   doi = {10.1109/ACCESS.2019.2938402},
   issn = {21693536},
   journal = {IEEE Access},
   keywords = {AEC-Net,Cobb angle estimation,Deep learning,Direct estimation,High-precision calculation},
   pages = {124596-124605},
   publisher = {Institute of Electrical and Electronics Engineers Inc.},
   title = {An automated and accurate spine curve analysis system},
   volume = {7},
   year = {2019},
}


@article{Lucic2017,
   abstract = {Generative adversarial networks (GAN) are a powerful subclass of generative models. Despite a very rich research activity leading to numerous interesting GAN algorithms, it is still very hard to assess which algorithm(s) perform better than others. We conduct a neutral, multi-faceted large-scale empirical study on state-of-the art models and evaluation measures. We find that most models can reach similar scores with enough hyperparameter optimization and random restarts. This suggests that improvements can arise from a higher computational budget and tuning more than fundamental algorithmic changes. To overcome some limitations of the current metrics, we also propose several data sets on which precision and recall can be computed. Our experimental results suggest that future GAN research should be based on more systematic and objective evaluation procedures. Finally, we did not find evidence that any of the tested algorithms consistently outperforms the non-saturating GAN introduced in \cite\{goodfellow2014generative\}.},
   author = {Mario Lucic and Karol Kurach and Marcin Michalski and Sylvain Gelly and Olivier Bousquet},
   month = {11},
   title = {Are GANs Created Equal? A Large-Scale Study},
   year = {2017},
}
@report{Arun21,
   abstract = {Saliency maps have become a widely used method to make deep learning models more interpretable by providing post-hoc explanations of classifiers through identification of the most pertinent areas of the input medical image. They are increasingly being used in medical imaging to provide clinically plausible explanations for the decisions the neural network makes. However, the utility and robustness of these visualization maps has not yet been rigorously examined in the context of medical imaging. We posit that trustworthiness in this context requires 1) localization utility, 2) sensitivity to model weight randomization, 3) repeatability, and 4) reproducibility. Using the localization information available in two large public radiology datasets, we quantify the performance of eight commonly used saliency map approaches for the above criteria using area under the precision-recall curves (AUPRC) and structural similarity index (SSIM), comparing their performance to various baseline measures. Using our framework to quantify the trustworthiness of saliency maps, we show that all eight saliency map techniques fail at least one of the criteria and are, in most cases, less trustworthy when compared to the baselines. We suggest that their usage in the high-risk domain of medical imaging warrants additional scrutiny and recommend that detection or segmentation models be used if localization is the desired output of the network. Deep learning has brought about many promising applications within medical imaging with recent studies showing its potential for key clinical assessments within dermatology, ophthalmology, pathology, oncology, cardiology, and radiology [1,2,3,4,5,6]. One major class of deep neural networks is convolutional neural networks (CNNs), which take raw pixel values as input, and transform them into the output of interest (such as diagnosis of disease [40] or expected clinical outcome [41]) after passing through many layers of non-linear transforms that provide the necessary capacity for higher levels of abstraction. Many of these fully trained CNNs have outperformed conventional methods for various † These authors contributed equally},
   author = {Nishanth Arun and Nathan Gaw and Praveer Singh and Ken Chang and Mehak Aggarwal and Bryan Chen and Katharina Hoebel and Sharut Gupta and Jay Patel and Mishka Gidwani and Julius Adebayo and Matthew D Li and Jayashree Kalpathy-Cramer},
   keywords = {Saliency maps · localization · deep learning},
   title = {Assessing the (Un)Trustworthiness of Saliency Maps for Localizing Abnormalities in Medical Imaging},
}
@article{He2017,
   abstract = {Facial attribute editing aims to manipulate single or multiple attributes of a face image, i.e., to generate a new face with desired attributes while preserving other details. Recently, generative adversarial net (GAN) and encoder-decoder architecture are usually incorporated to handle this task with promising results. Based on the encoder-decoder architecture, facial attribute editing is achieved by decoding the latent representation of the given face conditioned on the desired attributes. Some existing methods attempt to establish an attribute-independent latent representation for further attribute editing. However, such attribute-independent constraint on the latent representation is excessive because it restricts the capacity of the latent representation and may result in information loss, leading to over-smooth and distorted generation. Instead of imposing constraints on the latent representation, in this work we apply an attribute classification constraint to the generated image to just guarantee the correct change of desired attributes, i.e., to "change what you want". Meanwhile, the reconstruction learning is introduced to preserve attribute-excluding details, in other words, to "only change what you want". Besides, the adversarial learning is employed for visually realistic editing. These three components cooperate with each other forming an effective framework for high quality facial attribute editing, referred as AttGAN. Furthermore, our method is also directly applicable for attribute intensity control and can be naturally extended for attribute style manipulation. Experiments on CelebA dataset show that our method outperforms the state-of-the-arts on realistic attribute editing with facial details well preserved.},
   author = {Zhenliang He and Wangmeng Zuo and Meina Kan and Shiguang Shan and Xilin Chen},
   month = {11},
   title = {AttGAN: Facial Attribute Editing by Only Changing What You Want},
   year = {2017},
}
@article{Almahairi2018,
   abstract = {Learning inter-domain mappings from unpaired data can improve performance in structured prediction tasks, such as image segmentation, by reducing the need for paired data. CycleGAN was recently proposed for this problem, but critically assumes the underlying inter-domain mapping is approximately deterministic and one-to-one. This assumption renders the model ineffective for tasks requiring flexible, many-to-many mappings. We propose a new model, called Augmented CycleGAN, which learns many-to-many mappings between domains. We examine Augmented CycleGAN qualitatively and quantitatively on several image datasets.},
   author = {Amjad Almahairi and Sai Rajeswar and Alessandro Sordoni and Philip Bachman and Aaron Courville},
   month = {2},
   title = {Augmented CycleGAN: Learning Many-to-Many Mappings from Unpaired Data},
   year = {2018},
}
@article{Kingma2013,
   abstract = {How can we perform efficient inference and learning in directed probabilistic models, in the presence of continuous latent variables with intractable posterior distributions, and large datasets? We introduce a stochastic variational inference and learning algorithm that scales to large datasets and, under some mild differentiability conditions, even works in the intractable case. Our contributions is two-fold. First, we show that a reparameterization of the variational lower bound yields a lower bound estimator that can be straightforwardly optimized using standard stochastic gradient methods. Second, we show that for i.i.d. datasets with continuous latent variables per datapoint, posterior inference can be made especially efficient by fitting an approximate inference model (also called a recognition model) to the intractable posterior using the proposed lower bound estimator. Theoretical advantages are reflected in experimental results.},
   author = {Diederik P Kingma and Max Welling},
   month = {12},
   title = {Auto-Encoding Variational Bayes},
   year = {2013},
}
@article{Larsen2015,
   abstract = {We present an autoencoder that leverages learned representations to better measure similarities in data space. By combining a variational autoencoder with a generative adversarial network we can use learned feature representations in the GAN discriminator as basis for the VAE reconstruction objective. Thereby, we replace element-wise errors with feature-wise errors to better capture the data distribution while offering invariance towards e.g. translation. We apply our method to images of faces and show that it outperforms VAEs with element-wise similarity measures in terms of visual fidelity. Moreover, we show that the method learns an embedding in which high-level abstract visual features (e.g. wearing glasses) can be modified using simple arithmetic.},
   author = {Anders Boesen Lindbo Larsen and Søren Kaae Sønderby and Hugo Larochelle and Ole Winther},
   month = {12},
   title = {Autoencoding beyond pixels using a learned similarity metric},
   year = {2015},
}
@article{Tang2020,
   abstract = {As one of the most ubiquitous diagnostic imaging tests in medical practice, chest radiography requires timely reporting of potential findings and diagnosis of diseases in the images. Automated, fast, and reliable detection of diseases based on chest radiography is a critical step in radiology workflow. In this work, we developed and evaluated various deep convolutional neural networks (CNN) for differentiating between normal and abnormal frontal chest radiographs, in order to help alert radiologists and clinicians of potential abnormal findings as a means of work list triaging and reporting prioritization. A CNN-based model achieved an AUC of 0.9824 ± 0.0043 (with an accuracy of 94.64 ± 0.45},
   author = {Yu Xing Tang and You Bao Tang and Yifan Peng and Ke Yan and Mohammadhadi Bagheri and Bernadette A. Redd and Catherine J. Brandon and Zhiyong Lu and Mei Han and Jing Xiao and Ronald M. Summers},
   doi = {10.1038/s41746-020-0273-z},
   issn = {23986352},
   issue = {1},
   journal = {npj Digital Medicine},
   month = {12},
   publisher = {Nature Research},
   title = {Automated abnormality classification of chest radiographs using deep convolutional neural networks},
   volume = {3},
   year = {2020},
}

@inproceedings{He2017,
   abstract = {Lumbar disc degeneration (LDD) is a commonly age-associated condition related to low back pain, while its consequences are responsible for over 90.},
   author = {Xiaoxu He and Mark Landis and Stephanie Leung and James Warrington and Olga Shmuilovich and Shuo Li},
   doi = {10.1117/12.2253688},
   isbn = {9781510607132},
   issn = {16057422},
   journal = {Medical Imaging 2017: Computer-Aided Diagnosis},
   month = {3},
   pages = {1013443},
   publisher = {SPIE},
   title = {Automated grading of lumbar disc degeneration via supervised distance metric learning},
   volume = {10134},
   year = {2017},
}

@article{Almahairi2018,
   abstract = {Learning inter-domain mappings from unpaired data can improve performance in structured prediction tasks, such as image segmentation, by reducing the need for paired data. CycleGAN was recently proposed for this problem, but critically assumes the underlying inter-domain mapping is approximately deterministic and one-to-one. This assumption renders the model ineffective for tasks requiring flexible, many-to-many mappings. We propose a new model, called Augmented CycleGAN, which learns many-to-many mappings between domains. We examine Augmented CycleGAN qualitatively and quantitatively on several image datasets.},
   author = {Amjad Almahairi and Sai Rajeswar and Alessandro Sordoni and Philip Bachman and Aaron Courville},
   month = {2},
   title = {Augmented CycleGAN: Learning Many-to-Many Mappings from Unpaired Data},
   year = {2018},
}
@article{Kingma2013,
   abstract = {How can we perform efficient inference and learning in directed probabilistic models, in the presence of continuous latent variables with intractable posterior distributions, and large datasets? We introduce a stochastic variational inference and learning algorithm that scales to large datasets and, under some mild differentiability conditions, even works in the intractable case. Our contributions is two-fold. First, we show that a reparameterization of the variational lower bound yields a lower bound estimator that can be straightforwardly optimized using standard stochastic gradient methods. Second, we show that for i.i.d. datasets with continuous latent variables per datapoint, posterior inference can be made especially efficient by fitting an approximate inference model (also called a recognition model) to the intractable posterior using the proposed lower bound estimator. Theoretical advantages are reflected in experimental results.},
   author = {Diederik P Kingma and Max Welling},
   month = {12},
   title = {Auto-Encoding Variational Bayes},
   year = {2013},
}
@article{Larsen2015,
   abstract = {We present an autoencoder that leverages learned representations to better measure similarities in data space. By combining a variational autoencoder with a generative adversarial network we can use learned feature representations in the GAN discriminator as basis for the VAE reconstruction objective. Thereby, we replace element-wise errors with feature-wise errors to better capture the data distribution while offering invariance towards e.g. translation. We apply our method to images of faces and show that it outperforms VAEs with element-wise similarity measures in terms of visual fidelity. Moreover, we show that the method learns an embedding in which high-level abstract visual features (e.g. wearing glasses) can be modified using simple arithmetic.},
   author = {Anders Boesen Lindbo Larsen and Søren Kaae Sønderby and Hugo Larochelle and Ole Winther},
   month = {12},
   title = {Autoencoding beyond pixels using a learned similarity metric},
   year = {2015},
}
@article{Tang2020,
   abstract = {As one of the most ubiquitous diagnostic imaging tests in medical practice, chest radiography requires timely reporting of potential findings and diagnosis of diseases in the images. Automated, fast, and reliable detection of diseases based on chest radiography is a critical step in radiology workflow. In this work, we developed and evaluated various deep convolutional neural networks (CNN) for differentiating between normal and abnormal frontal chest radiographs, in order to help alert radiologists and clinicians of potential abnormal findings as a means of work list triaging and reporting prioritization. A CNN-based model achieved an AUC of 0.9824 ± 0.0043 (with an accuracy of 94.64 ± 0.45},
   author = {Yu Xing Tang and You Bao Tang and Yifan Peng and Ke Yan and Mohammadhadi Bagheri and Bernadette A. Redd and Catherine J. Brandon and Zhiyong Lu and Mei Han and Jing Xiao and Ronald M. Summers},
   doi = {10.1038/s41746-020-0273-z},
   issn = {23986352},
   issue = {1},
   journal = {npj Digital Medicine},
   month = {12},
   publisher = {Nature Research},
   title = {Automated abnormality classification of chest radiographs using deep convolutional neural networks},
   volume = {3},
   year = {2020},
}
@article{Chamveha2020,
   abstract = {We propose an algorithm for calculating the cardiothoracic ratio (CTR) from chest X-ray films. Our approach applies a deep learning model based on U-Net with VGG16 encoder to extract lung and heart masks from chest X-ray images and calculate CTR from the extents of obtained masks. Human radiologists evaluated our CTR measurements, and $76.5\%$ were accepted to be included in medical reports without any need for adjustment. This result translates to a large amount of time and labor saved for radiologists using our automated tools.},
   author = {Isarun Chamveha and Treethep Promwiset and Trongtum Tongdee and Pairash Saiviroonporn and Warasinee Chaisangmongkon},
   month = {2},
   journal = {arXiv},
   title = {Automated Cardiothoracic Ratio Calculation and Cardiomegaly Detection using Deep Learning Approach},
   year = {2020},
}
@inproceedings{He2017,
   abstract = {Lumbar disc degeneration (LDD) is a commonly age-associated condition related to low back pain, while its consequences are responsible for over 90.},
   author = {Xiaoxu He and Mark Landis and Stephanie Leung and James Warrington and Olga Shmuilovich and Shuo Li},
   doi = {10.1117/12.2253688},
   isbn = {9781510607132},
   issn = {16057422},
   journal = {Medical Imaging 2017: Computer-Aided Diagnosis},
   month = {3},
   pages = {1013443},
   publisher = {SPIE},
   title = {Automated grading of lumbar disc degeneration via supervised distance metric learning},
   volume = {10134},
   year = {2017},
}


@article{Tu2019,
    abstract = {Aiming at the subjective experience of the physician, the high measurement error in the Cobb angle measurement of scoliosis X-ray images and the X-ray image of spine is difficult to segment. A deep learning based scoliosis Cobb angle measurement algorithm which can automatically calculate Cobb angle without the physician's manual definition is proposed. A DU-Net detection and segmentation network is proposed in this paper to remove the unrelated regions and to segment the spine contour in the spine X-ray image. The aggregated channel features in pedestrian detection algorithm is introduced to scoliosis image to realize the spine region detection. And the DU-Net network is training to segment spine contour. Therefore, the spine curve can be fitted by the spine contour and the Cobb angle can be automatically measured by the tangent line of spine curve. As a result, the Cobb angle measure methods yields an average error of 2.9° to reference Cobb angle which are measured manually by special orthopaedist. The detection algorithm in this paper yields an average precision of 98.5.},
    author = {Yongcheng Tu and Nian Wang and Fei Tong and Hemu Chen},
    doi = {10.1088/1742-6596/1187/4/042100},
    issn = {17426596},
    issue = {4},
    journal = {Journal of Physics: Conference Series},
    month = {5},
    publisher = {Institute of Physics Publishing},
    title = {Automatic measurement algorithm of scoliosis Cobb angle based on deep learning},
    volume = {1187},
    year = {2019},
 }
 @article{Kim2020,
    abstract = {In this paper, an automated and visually explainable system is proposed for a scoliosis assessment from spinal radiographs, which deals with the drawback of manual measurements, which are known to be time-consuming, cumbersome, and operator dependent. Deep learning techniques have been successfully applied in the accurate extraction of Cobb angle measurements, which is the gold standard for a scoliosis assessment. Such deep learning methods directly estimate the Cobb angle without providing structural information of the spine which can be used for diagnosis. Although conventional segmentation-based methods can provide the spine structure, they still have limitations in the accurate measurement of the Cobb angle. It would be desirable to build a clinician-friendly diagnostic system for scoliosis that provides not only an automated Cobb angle assessment but also local and global structural information of the spine. This paper addresses this need through the development of a hierarchical method which consisting of three major parts. (1) A confidence map is used to selectively localize and identify all vertebrae in an accurate and robust manner, (2) vertebral-tilt field is used to estimate the slope of an individual vertebra, and (3) the Cobb angle is determined by combining the vertebral centroids with the previously obtained vertebral-tilt field. The performance of the proposed method was validated, resulting in circular mean absolute error of 3.51° and symmetric mean absolute percentage error of 7.84.},
    author = {Kang Cheol Kim and Hye Sun Yun and Sungjun Kim and Jin Keun Seo},
    doi = {10.1109/ACCESS.2020.2992081},
    issn = {21693536},
    journal = {IEEE Access},
    keywords = {Cobb angle,Frontal radiographs,deep learning,scoliosis,spine},
    pages = {84618-84630},
    publisher = {Institute of Electrical and Electronics Engineers Inc.},
    title = {Automation of Spine Curve Assessment in Frontal Radiographs Using Deep Learning of Vertebral-Tilt Vector},
    volume = {8},
    year = {2020},
 }

@inproceedings{Que2018,
  title={CardioXNet: automated detection for cardiomegaly based on deep learning},
  author={Que, Qiwen and Tang, Ze and Wang, Ruoshi and Zeng, Zeng and Wang, Jie and Chua, Matthew and Gee, Teo Sin and Yang, Xulei and Veeravalli, Bharadwaj},
  booktitle={2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)},
  pages={612--615},
  year={2018},
  organization={IEEE}
}

@inproceedings{OdenaOlah2016,
  title={Conditional image synthesis with auxiliary classifier gans},
  author={Odena, Augustus and Olah, Christopher and Shlens, Jonathon},
  booktitle={International conference on machine learning},
  pages={2642--2651},
  year={2017},
  organization={PMLR}
}

@article{Lee2017,
    abstract = {Recently introduced generative adversarial network (GAN) has been shown numerous promising results to generate realistic samples. The essential task of GAN is to control the features of samples generated from a random distribution. While the current GAN structures, such as conditional GAN, successfully generate samples with desired major features, they often fail to produce detailed features that bring specific differences among samples. To overcome this limitation, here we propose a controllable GAN (ControlGAN) structure. By separating a feature classifier from a discriminator, the generator of ControlGAN is designed to learn generating synthetic samples with the specific detailed features. Evaluated with multiple image datasets, ControlGAN shows a power to generate improved samples with well-controlled features. Furthermore, we demonstrate that ControlGAN can generate intermediate features and opposite features for interpolated and extrapolated input labels that are not used in the training process. It implies that ControlGAN can significantly contribute to the variety of generated samples.},
    author = {Minhyeok Lee and Junhee Seok},
    month = {8},
    title = {Controllable Generative Adversarial Network},
    year = {2017},
 }

 @article{Horng2020,
    abstract = {Background: Clinical management decisions for acutely decompensated CHF patients are often based on grades of pulmonary edema severity, rather than its mere absence or presence. The grading of pulmonary edema on chest radiographs is based on well-known radiologic findings. Purpose: We develop a clinical machine learning task to grade pulmonary edema severity and release both the underlying data and code to serve as a benchmark for future algorithmic developments in machine vision. Materials and Methods: We collected 369,071 chest radiographs and their associated radiology reports from 64,581 patients from the MIMIC-CXR chest radiograph dataset. We extracted pulmonary edema severity labels from the associated radiology reports as 4 ordinal levels: no edema (0), vascular congestion (1), interstitial edema (2), and alveolar edema (3). We developed machine learning models using two standard approaches: 1) a semi-supervised model using a variational autoencoder and 2) a pre-trained supervised learning model using a dense neural network. Results: We measured the area under the receiver operating characteristic curve (AUROC) from the semi-supervised model and the pre-trained model. AUROC for differentiating alveolar edema from no edema was 0.99 and 0.87 (semi-supervised and pre-trained models). Performance of the algorithm was inversely related to the difficulty in categorizing milder states of pulmonary edema: 2 vs 0 (0.88, 0.81), 1 vs 0 (0.79, 0.66), 3 vs 1 (0.93, 0.82), 2 vs 1 (0.69, 0.73), 3 vs 2 (0.88, 0.63). Conclusion: Accurate grading of pulmonary edema on chest radiographs is a clinically important task. Application of state-of-the-art machine learning techniques can produce a novel quantitative imaging biomarker from one of the oldest and most widely available imaging modalities.},
    author = {Steven Horng and Ruizhi Liao and Xin Wang and Sandeep Dalal and Polina Golland and Seth J. Berkowitz},
    doi = {10.1148/ryai.2021190228},
    issn = {23318422},
    journal = {arXiv},
    month = {8},
    publisher = {arXiv},
    title = {Deep learning to quantify pulmonary edema in chest radiographs},
    year = {2020},
 }
 @article{Schlegl2019,
    abstract = {Obtaining expert labels in clinical imaging is difficult since exhaustive annotation is time-consuming. Furthermore, not all possibly relevant markers may be known and sufficiently well described a priori to even guide annotation. While supervised learning yields good results if expert labeled training data is available, the visual variability, and thus the vocabulary of findings, we can detect and exploit, is limited to the annotated lesions. Here, we present fast AnoGAN (f-AnoGAN), a generative adversarial network (GAN) based unsupervised learning approach capable of identifying anomalous images and image segments, that can serve as imaging biomarker candidates. We build a generative model of healthy training data, and propose and evaluate a fast mapping technique of new data to the GAN's latent space. The mapping is based on a trained encoder, and anomalies are detected via a combined anomaly score based on the building blocks of the trained model – comprising a discriminator feature residual error and an image reconstruction error. In the experiments on optical coherence tomography data, we compare the proposed method with alternative approaches, and provide comprehensive empirical evidence that f-AnoGAN outperforms alternative approaches and yields high anomaly detection accuracy. In addition, a visual Turing test with two retina experts showed that the generated images are indistinguishable from real normal retinal OCT images. The f-AnoGAN code is available at https://github.com/tSchlegl/f-AnoGAN.},
    author = {Thomas Schlegl and Philipp Seeböck and Sebastian M. Waldstein and Georg Langs and Ursula Schmidt-Erfurth},
    doi = {10.1016/j.media.2019.01.010},
    issn = {13618423},
    journal = {Medical Image Analysis},
    keywords = {Anomaly detection,Optical coherence tomography,Unsupervised learning,Wasserstein generative adversarial network},
    month = {5},
    pages = {30-44},
    pmid = {30831356},
    publisher = {Elsevier B.V.},
    title = {f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks},
    volume = {54},
    year = {2019},
 }

 @article{Schroff2015,
    abstract = {Despite significant recent advances in the field of face recognition [10, 14, 15, 17], implementing face verification and recognition efficiently at scale presents serious challenges to current approaches. In this paper we present a system, called FaceNet, that directly learns a mapping from face images to a compact Euclidean space where distances directly correspond to a measure of face similarity. Once this space has been produced, tasks such as face recognition, verification and clustering can be easily implemented using standard techniques with FaceNet embeddings as feature vectors. Our method uses a deep convolutional network trained to directly optimize the embedding itself, rather than an intermediate bottleneck layer as in previous deep learning approaches. To train, we use triplets of roughly aligned matching / non-matching face patches generated using a novel online triplet mining method. The benefit of our approach is much greater representational efficiency: we achieve state-of-the-art face recognition performance using only 128-bytes per face. On the widely used Labeled Faces in the Wild (LFW) dataset, our system achieves a new record accuracy of 99.63.},
    author = {Florian Schroff and Dmitry Kalenichenko and James Philbin},
    doi = {10.1109/CVPR.2015.7298682},
    isbn = {9781467369640},
    issn = {10636919},
    journal = {Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition},
    month = {10},
    pages = {815-823},
    publisher = {IEEE Computer Society},
    title = {FaceNet: A unified embedding for face recognition and clustering},
    volume = {07-12-June-2015},
    year = {2015},
 }


 @article{Yi2019,
    abstract = {Generative adversarial networks have gained a lot of attention in the computer vision community due to their capability of data generation without explicitly modelling the probability density function. The adversarial loss brought by the discriminator provides a clever way of incorporating unlabeled samples into training and imposing higher order consistency. This has proven to be useful in many cases, such as domain adaptation, data augmentation, and image-to-image translation. These properties have attracted researchers in the medical imaging community, and we have seen rapid adoption in many traditional and novel applications, such as image reconstruction, segmentation, detection, classification, and cross-modality synthesis. Based on our observations, this trend will continue and we therefore conducted a review of recent advances in medical imaging using the adversarial training scheme with the hope of benefiting researchers interested in this technique.},
    author = {Xin Yi and Ekta Walia and Paul Babyn},
    doi = {10.1016/j.media.2019.101552},
    issn = {13618423},
    journal = {Medical Image Analysis},
    keywords = {Deep learning,Generative adversarial network,Generative model,Medical imaging,Review},
    month = {12},
    pmid = {31521965},
    publisher = {Elsevier B.V.},
    title = {Generative adversarial network in medical imaging: A review},
    volume = {58},
    year = {2019},
}

 @article{Pesce2019,
    abstract = {Machine learning approaches hold great potential for the automated detection of lung nodules on chest radiographs, but training algorithms requires very large amounts of manually annotated radiographs, which are difficult to obtain. The increasing availability of PACS (Picture Archiving and Communication System), is laying the technological foundations needed to make available large volumes of clinical data and images from hospital archives. Binary labels indicating whether a radiograph contains a pulmonary lesion can be extracted at scale, using natural language processing algorithms. In this study, we propose two novel neural networks for the detection of chest radiographs containing pulmonary lesions. Both architectures make use of a large number of weakly-labelled images combined with a smaller number of manually annotated x-rays. The annotated lesions are used during training to deliver a type of visual attention feedback informing the networks about their lesion localisation performance. The first architecture extracts saliency maps from high-level convolutional layers and compares the inferred position of a lesion against the true position when this information is available; a localisation error is then back-propagated along with the softmax classification error. The second approach consists of a recurrent attention model that learns to observe a short sequence of smaller image portions through reinforcement learning; the reward function penalises the exploration of areas, within an image, that are unlikely to contain nodules. Using a repository of over 430,000 historical chest radiographs, we present and discuss the proposed methods over related architectures that use either weakly-labelled or annotated images only.},
    author = {Emanuele Pesce and Samuel Joseph Withey and Petros Pavlos Ypsilantis and Robert Bakewell and Vicky Goh and Giovanni Montana},
    doi = {10.1016/j.media.2018.12.007},
    issn = {13618423},
    journal = {Medical Image Analysis},
    keywords = {Deep learning,Image classification,Lung cancer,Object localisation,Visual attention,X-rays},
    month = {4},
    pages = {26-38},
    pmid = {30660946},
    publisher = {Elsevier B.V.},
    title = {Learning to detect chest radiographs containing pulmonary lesions using visual attention networks},
    volume = {53},
    year = {2019},
 }

 @article{Jiao2021,
    abstract = {BACKGROUND Chest x-ray is a relatively accessible, inexpensive, fast imaging modality that might be valuable in the prognostication of patients with COVID-19. We aimed to develop and evaluate an artificial intelligence system using chest x-rays and clinical data to predict disease severity and progression in patients with COVID-19. METHODS We did a retrospective study in multiple hospitals in the University of Pennsylvania Health System in Philadelphia, PA, USA, and Brown University affiliated hospitals in Providence, RI, USA. Patients who presented to a hospital in the University of Pennsylvania Health System via the emergency department, with a diagnosis of COVID-19 confirmed by RT-PCR and with an available chest x-ray from their initial presentation or admission, were retrospectively identified and randomly divided into training, validation, and test sets (7:1:2). Using the chest x-rays as input to an EfficientNet deep neural network and clinical data, models were trained to predict the binary outcome of disease severity (ie, critical or non-critical). The deep-learning features extracted from the model and clinical data were used to build time-to-event models to predict the risk of disease progression. The models were externally tested on patients who presented to an independent multicentre institution, Brown University affiliated hospitals, and compared with severity scores provided by radiologists. FINDINGS 1834 patients who presented via the University of Pennsylvania Health System between March 9 and July 20, 2020, were identified and assigned to the model training (n=1285), validation (n=183), or testing (n=366) sets. 475 patients who presented via the Brown University affiliated hospitals between March 1 and July 18, 2020, were identified for external testing of the models. When chest x-rays were added to clinical data for severity prediction, area under the receiver operating characteristic curve (ROC-AUC) increased from 0·821 (95)},
    author = {Zhicheng Jiao and Ji Whae Choi and Kasey Halsey and Thi My Linh Tran and Ben Hsieh and Dongcui Wang and Feyisope Eweje and Robin Wang and Ken Chang and Jing Wu and Scott A Collins and Thomas Y Yi and Andrew T Delworth and Tao Liu and Terrance T Healey and Shaolei Lu and Jianxin Wang and Xue Feng and Michael K Atalay and Li Yang and Michael Feldman and Paul J L Zhang and Wei-Hua Liao and Yong Fan and Harrison X Bai},
    doi = {10.1016/s2589-7500(21)00039-x},
    issn = {25897500},
    journal = {The Lancet Digital Health},
    month = {5},
    pmid = {33773969},
    publisher = {Elsevier BV},
    title = {Prognostication of patients with COVID-19 using artificial intelligence based on chest x-rays and clinical data: a retrospective study},
    year = {2021},
 }

@article{Qin2020,
    abstract = {Due to its importance in clinical science, the estimation of physiological states (e.g., the severity of pathological tremor) has aroused growing interest in machine learning community. While the physiological state is a continuous variable, its continuity is lost when the physiological state is quantized into a few discrete classes during recording and labeling. The discreteness introduces misalignment between the true value and its label, meaning that these labels are unfortunately imprecise and coarse-grained. Most previous work did not consider the inaccuracy and directly utilized the coarse labels to train the machine learning algorithms, whose predictions are also coarse-grained. In this work, we propose to learn a precise, fine-grained estimation of physiological states using these coarse-grained ground truths. Established on mathematical rigorous proof, we utilize imprecise labels to restore the probabilistic distribution of precise labels in an approximate order-preserving fashion, then the deep neural network learns from this distribution and offers fine-grained estimation. We demonstrate the effectiveness of our approach in assessing the pathological tremor in Parkinson’s Disease and estimating the systolic blood pressure from bioelectrical signals.},
    author = {Zengyi Qin and Jiansheng Chen and Zhenyu Jiang and Xumin Yu and Chunhua Hu and Yu Ma and Suhua Miao and Rongsong Zhou},
    doi = {10.1038/s41598-020-79007-5},
    issn = {20452322},
    issue = {1},
    journal = {Scientific Reports},
    month = {12},
    pmid = {33319835},
    publisher = {Nature Research},
    title = {Learning fine-grained estimation of physiological states from coarse-grained labels by distribution restoration},
    volume = {10},
    year = {2020},
 }

@article{Larson2018,
    abstract = {Purpose: To compare the performance of a deep-learning bone age assessment model based on hand radiographs with that of expert radiologists and that of existing automated models. Materials and Methods: The institutional review board approved the study. A total of 14 036 clinical hand radiographs and corresponding reports were obtained from two children's hospitals to train and validate the model. For the first test set, composed of 200 examinations, the mean of bone age estimates from the clinical report and three additional human reviewers was used as the reference standard. Overall model performance was assessed by comparing the root mean square (RMS) and mean absolute difference (MAD) between the model estimates and the reference standard bone ages. Ninety-five percent limits of agreement were calculated in a pairwise fashion for all reviewers and the model. The RMS of a second test set composed of 913 examinations from the publicly available Digital Hand Atlas was compared with published reports of an existing automated model. Results: The mean difference between bone age estimates of the model and of the reviewers was 0 years, with a mean RMS and MAD of 0.63 and 0.50 years, respectively. The estimates of the model, the clinical report, and the three reviewers were within the 95},
    author = {David B. Larson and Matthew C. Chen and Matthew P. Lungren and Safwan S. Halabi and Nicholas V. Stence and Curtis P. Langlotz},
    doi = {10.1148/radiol.2017170236},
    issn = {15271315},
    issue = {1},
    journal = {Radiology},
    month = {4},
    pages = {313-322},
    pmid = {29095675},
    publisher = {Radiological Society of North America Inc.},
    title = {Performance of a deep-learning neural network model in assessing skeletal maturity on pediatric hand radiographs},
    volume = {287},
    year = {2018},
 }

 @article{Rothe2018,
    abstract = {In this paper we propose a deep learning solution to age estimation from a single face image without the use of facial landmarks and introduce the IMDB-WIKI dataset, the largest public dataset of face images with age and gender labels. If the real age estimation research spans over decades, the study of apparent age estimation or the age as perceived by other humans from a face image is a recent endeavor. We tackle both tasks with our convolutional neural networks (CNNs) of VGG-16 architecture which are pre-trained on ImageNet for image classification. We pose the age estimation problem as a deep classification problem followed by a softmax expected value refinement. The key factors of our solution are: deep learned models from large data, robust face alignment, and expected value formulation for age regression. We validate our methods on standard benchmarks and achieve state-of-the-art results for both real and apparent age estimation.},
    author = {Rasmus Rothe and Radu Timofte and Luc Van Gool},
    doi = {10.1007/s11263-016-0940-3},
    issn = {15731405},
    issue = {2-4},
    journal = {International Journal of Computer Vision},
    keywords = {Age estimation,CNN,Deep learning,Regression},
    month = {4},
    pages = {144-157},
    publisher = {Springer New York LLC},
    title = {Deep Expectation of Real and Apparent Age from a Single Image Without Facial Landmarks},
    volume = {126},
    year = {2018},
 }

 @article{Niu2016,
    abstract = {To address the non-stationary property of aging patterns, age estimation can be cast as an ordinal regression problem. However, the processes of extracting features and learning a regression model are often separated and optimized independently in previous work. In this paper, we propose an End-to-End learning approach to address ordinal regression problems using deep Convolutional Neural Network, which could simultaneously conduct feature learning and regression modeling. In particular, an ordinal regression problem is transformed into a series of binary classification sub-problems. And we propose a multiple output CNN learning algorithm to collectively solve these classification sub-problems, so that the correlation between these tasks could be explored. In addition, we publish an Asian Face Age Dataset (AFAD) containing more than 160K facial images with precise age ground-truths, which is the largest public age dataset to date. To the best of our knowledge, this is the first work to address ordinal regression problems by using CNN, and achieves the state-of-the-art performance on both the MORPH and AFAD datasets.},
    author = {Zhenxing Niu and Mo Zhou and Le Wang and Xinbo Gao and Gang Hua},
    doi = {10.1109/CVPR.2016.532},
    isbn = {9781467388504},
    issn = {10636919},
    journal = {Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition},
    month = {12},
    pages = {4920-4928},
    publisher = {IEEE Computer Society},
    title = {Ordinal regression with multiple output CNN for age estimation},
    volume = {2016-December},
    year = {2016},
 }


@article{horng2021,
  title={Deep learning to quantify pulmonary edema in chest radiographs},
  author={Horng, Steven and Liao, Ruizhi and Wang, Xin and Dalal, Sandeep and Golland, Polina and Berkowitz, Seth J},
  journal={Radiology: Artificial Intelligence},
  volume={3},
  number={2},
  pages={e190228},
  year={2021},
  publisher={Radiological Society of North America}
}

 @article{Singla2019,
    author = {Sumedha Singla and Brian Pollack and Junxiang Chen and Kayhan Batmanghelich},
    month = {11},
    journal={arXiv preprint arXiv:1911.00483},
    title = {Explanation by Progressive Exaggeration},
    year = {2019},
 }

@article{cohen2020,
  title={Predicting covid-19 pneumonia severity on chest x-ray with deep learning},
  author={Cohen, Joseph Paul and Dao, Lan and Roth, Karsten and Morrison, Paul and Bengio, Yoshua and Abbasi, Almas F and Shen, Beiyi and Mahsa, Hoshmand Kochi and Ghassemi, Marzyeh and Li, Haifang and others},
  journal={Cureus},
  volume={12},
  number={7},
  year={2020},
  publisher={Cureus}
}

@article{Springenberg2014,
  title={Striving for simplicity: The all convolutional net},
  author={Springenberg, Jost Tobias and Dosovitskiy, Alexey and Brox, Thomas and Riedmiller, Martin},
  journal={arXiv preprint arXiv:1412.6806},
  year={2014}
}

 @article{Packhauser21,
    abstract = {With the rise and ever-increasing potential of deep learning techniques in recent years, publicly available medical datasets became a key factor to enable reproducible development of diagnostic algorithms in the medical domain. Medical data contains sensitive patient-related information and is therefore usually anonymized by removing patient identifiers, e.g., patient names before publication. To the best of our knowledge, we are the first to show that a well-trained deep learning system is able to recover the patient identity from chest X-ray data. We demonstrate this using the publicly available large-scale ChestX-ray14 dataset, a collection of 112,120 frontal-view chest X-ray images from 30,805 unique patients. Our verification system is able to identify whether two frontal chest X-ray images are from the same person with an AUC of 0.9940 and a classification accuracy of 95.55. We further highlight that the proposed system is able to reveal the same person even ten and more years after the initial scan. When pursuing a retrieval approach, we observe an mAP@R of 0.9748 and a precision@1 of 0.9963. Furthermore, we achieve an AUC of up to 0.9870 and a precision@1 of up to 0.9444 when evaluating our trained networks on external datasets such as CheXpert and the COVID-19 Image Data Collection. Based on this high identification rate, a potential attacker may leak patient-related information and additionally cross-reference images to obtain more information. Thus, there is a great risk of sensitive content falling into unauthorized hands or being disseminated against the will of the concerned patients. Especially during the COVID-19 pandemic, numerous chest X-ray datasets have been published to advance research. Therefore, such data may be vulnerable to potential attacks by deep learning-based re-identification algorithms.},
    author = {Kai Packhäuser and Sebastian Gündel and Nicolas Münster and Christopher Syben and Vincent Christlein and Andreas Maier},
    doi = {10.1038/s41598-022-19045-3},
    issn = {20452322},
    issue = {1},
    journal = {Scientific Reports},
    month = {12},
    pmid = {36050406},
    publisher = {Nature Research},
    title = {Deep learning-based patient re-identification is able to exploit the biometric nature of medical chest X-ray data},
    volume = {12},
    year = {2022},
 }

 @article{Liu2022,
    abstract = {Disentangled representation learning has been proposed as an approach to learning general representations even in the absence of, or with limited, supervision. A good general representation can be fine-tuned for new target tasks using modest amounts of data, or used directly in unseen domains achieving remarkable performance in the corresponding task. This alleviation of the data and annotation requirements offers tantalising prospects for applications in computer vision and healthcare. In this tutorial paper, we motivate the need for disentangled representations, revisit key concepts, and describe practical building blocks and criteria for learning such representations. We survey applications in medical imaging emphasising choices made in exemplar key works, and then discuss links to computer vision applications. We conclude by presenting limitations, challenges, and opportunities.},
    author = {Xiao Liu and Pedro Sanchez and Spyridon Thermos and Alison Q. O’Neil and Sotirios A. Tsaftaris},
    doi = {10.1016/j.media.2022.102516},
    issn = {13618415},
    journal = {Medical Image Analysis},
    month = {8},
    pages = {102516},
    publisher = {Elsevier BV},
    title = {Learning disentangled representations in the imaging domain},
    volume = {80},
    year = {2022},
 }

 @article{Song2022,
    abstract = {This article addresses two crucial problems of learning disentangled image representations, namely, controlling the degree of disentanglement during image editing, and balancing the disentanglement strength and the reconstruction quality. To encourage disentanglement, we devise distance covariance-based decorrelation regularization. Further, for the reconstruction step, our model leverages a soft target representation combined with the latent image code. By exploring the real-valued space of the soft target representation, we are able to synthesize novel images with the designated properties. To improve the perceptual quality of images generated by autoencoder (AE)-based models, we extend the encoder-decoder architecture with the generative adversarial network (GAN) by collapsing the AE decoder and the GAN generator into one. We also design a classification-based protocol to quantitatively evaluate the disentanglement strength of our model. The experimental results showcase the benefits of the proposed model.},
    author = {Zengjie Song and Oluwasanmi Koyejo and Jiangshe Zhang},
    doi = {10.1109/TCYB.2020.3000480},
    issn = {21682275},
    issue = {4},
    journal = {IEEE Transactions on Cybernetics},
    keywords = {Autoencoder (AE),Decorrelation regularization,Generative adversarial network (GAN),Image generation,Representation learning},
    month = {4},
    pages = {2491-2504},
    pmid = {32667884},
    publisher = {Institute of Electrical and Electronics Engineers Inc.},
    title = {Toward a Controllable Disentanglement Network},
    volume = {52},
    year = {2022},
 }
 @article{Szekely2007,
    abstract = {Distance correlation is a new measure of dependence between random vectors. Distance covariance and distance correlation are analogous to product-moment covariance and correlation, but unlike the classical definition of correlation, distance correlation is zero only if the random vectors are independent. The empirical distance dependence measures are based on certain Euclidean distances between sample elements rather than sample moments, yet have a compact representation analogous to the classical covariance and correlation. Asymptotic properties and applications in testing independence are discussed. Implementation of the test and Monte Carlo results are also presented. © Institute of Mathematical Statistics, 2007.},
    author = {Gábor J. Székely and Maria L. Rizzo and Nail K. Bakirov},
    doi = {10.1214/009053607000000505},
    issn = {00905364},
    issue = {6},
    journal = {Annals of Statistics},
    keywords = {Distance correlation,Distance covariance,Multivariate independence},
    month = {12},
    pages = {2769-2794},
    title = {Measuring and testing dependence by correlation of distances},
    volume = {35},
    year = {2007},
 }

 @incollection{iglovikov2018,
   title={Paediatric bone age assessment using deep convolutional neural networks},
   author={Iglovikov, Vladimir I and Rakhlin, Alexander and Kalinin, Alexandr A and Shvets, Alexey A},
   booktitle={Deep learning in medical image analysis and multimodal learning for clinical decision support},
   pages={300--308},
   year={2018},
   publisher={Springer}
 }


 @article{Kortli2020,
    abstract = {Over the past few decades, interest in theories and algorithms for face recognition has been growing rapidly. Video surveillance, criminal identification, building access control, and unmanned and autonomous vehicles are just a few examples of concrete applications that are gaining attraction among industries. Various techniques are being developed including local, holistic, and hybrid approaches, which provide a face image description using only a few face image features or the whole facial features. The main contribution of this survey is to review some well-known techniques for each approach and to give the taxonomy of their categories. In the paper, a detailed comparison between these techniques is exposed by listing the advantages and the disadvantages of their schemes in terms of robustness, accuracy, complexity, and discrimination. One interesting feature mentioned in the paper is about the database used for face recognition. An overview of the most commonly used databases, including those of supervised and unsupervised learning, is given. Numerical results of the most interesting techniques are given along with the context of experiments and challenges handled by these techniques. Finally, a solid discussion is given in the paper about future directions in terms of techniques to be used for face recognition.},
    author = {Yassin Kortli and Maher Jridi and Ayman Al Falou and Mohamed Atri},
    doi = {10.3390/s20020342},
    issn = {14248220},
    issue = {2},
    journal = {Sensors (Switzerland)},
    keywords = {Biometric systems,Face recognition systems,Person identification,Survey},
    month = {1},
    pmid = {31936089},
    publisher = {MDPI AG},
    title = {Face recognition systems: A survey},
    volume = {20},
    year = {2020},
 }
 @article{Wang2021,
    abstract = {Deep learning applies multiple processing layers to learn representations of data with multiple levels of feature extraction. This emerging technique has reshaped the research landscape of face recognition (FR) since 2014, launched by the breakthroughs of DeepFace and DeepID. Since then, deep learning technique, characterized by the hierarchical architecture to stitch together pixels into invariant face representation, has dramatically improved the state-of-the-art performance and fostered successful real-world applications. In this survey, we provide a comprehensive review of the recent developments on deep FR, covering broad topics on algorithm designs, databases, protocols, and application scenes. First, we summarize different network architectures and loss functions proposed in the rapid evolution of the deep FR methods. Second, the related face processing methods are categorized into two classes: “one-to-many augmentation” and “many-to-one normalization”. Then, we summarize and compare the commonly used databases for both model training and evaluation. Third, we review miscellaneous scenes in deep FR, such as cross-factor, heterogenous, multiple-media and industrial scenes. Finally, the technical challenges and several promising directions are highlighted.},
    author = {Mei Wang and Weihong Deng},
    doi = {10.1016/j.neucom.2020.10.081},
    issn = {18728286},
    journal = {Neurocomputing},
    keywords = {Deep face recognition,Deep learning,Deep network architecture,Face processing,Face recognition database,Loss function},
    month = {3},
    pages = {215-244},
    publisher = {Elsevier B.V.},
    title = {Deep face recognition: A survey},
    volume = {429},
    year = {2021},
 }

@article{Noumeir2007,
    abstract = {Medical image processing methods and algorithms, developed by researchers, need to be validated and tested. Test data would ideally be real clinical data especially that clinical data is varied and exists in large volumes. Nowadays, clinical data is accessible electronically and has important value for researchers. However, the usage of clinical data for research purposes should respect data confidentiality, patient right to privacy, and patient consent. In fact, clinical data is nominative given that it contains information about the patient such as name, age, and identification number. Evidently, clinical data needs to be de-identified to be exported to research databases. However, the same patient is usually followed during a long period of time. The disease progression and the diagnostic evolution represent extremely valuable information for researchers as well. Our objective is to build a research database from de-identified clinical data while enabling the data set to be easily incremented by exporting new pseudonymous data, acquired over a long period of time. Pseudonymization is data de-identification, such that data belonging to an individual in the clinical environment still belong to the same individual in the de-identified research version. In this paper, we explore various software architectures to enable the implementation of an imaging research database that can be incremented in time. We also evaluate their security and discuss their security pitfalls. As most imaging data accessible electronically is available with the digital imaging and communication in medicine (DICOM) standard, we propose a de-identification scheme that closely follows DICOM recommendations. Our work can be used to enable electronic health record (EHR) secondary usage such as public surveillance and research, while maintaining patient confidentiality. © 2007 SCAR (Society for Computer Applications in Radiology).},
    author = {Rita Noumeir and Alain Lemay and Jean Marc Lina},
    doi = {10.1007/s10278-006-1051-4},
    issn = {08971889},
    issue = {3},
    journal = {Journal of Digital Imaging},
    keywords = {Confidentiality,De-identification,Medical imaging,Nominative health care data,Privacy,Pseudonymization,Radiology,Research database,Security},
    month = {9},
    pages = {284-295},
    pmid = {17191099},
    title = {Pseudonymization of radiology data for research purposes},
    volume = {20},
    year = {2007},
 }

 @article{Schwarz2019,
    abstract = {A, Volume rendering from the original nonedited CT scan. B, Volume rendering from the edited CT image does not display any unique distinguishing facial features. smoothed using a Gaussian smoothing method with a standard deviation of 5 mm. Then, a surface model was generated from the dilated and smoothed segmentation to replace the original air and skin voxels in the original CT scan. This method created a new DICOM volume with the intracranial anatomy preserved (Figure; Movie [online]).},
    author = {Christopher G. Schwarz and Walter K. Kremers and Terry M. Therneau and Richard R. Sharp and Jeffrey L. Gunter and Prashanthi Vemuri and Arvin Arani and Anthony J. Spychalla and Kejal Kantarci and David S. Knopman and Ronald C. Petersen and Clifford R. Jack},
    doi = {10.1056/nejmc1908881},
    issn = {0028-4793},
    issue = {17},
    journal = {New England Journal of Medicine},
    month = {10},
    pages = {1684-1686},
    pmid = {31644852},
    publisher = {Massachusetts Medical Society},
    title = {Identification of Anonymous MRI Research Participants with Face-Recognition Software},
    volume = {381},
    year = {2019},
 }
 @article{Xia2020,
    abstract = {Pseudo-healthy synthesis is the task of creating a subject-specific ‘healthy’ image from a pathological one. Such images can be helpful in tasks such as anomaly detection and understanding changes induced by pathology and disease. In this paper, we present a model that is encouraged to disentangle the information of pathology from what seems to be healthy. We disentangle what appears to be healthy and where disease is as a segmentation map, which are then recombined by a network to reconstruct the input disease image. We train our models adversarially using either paired or unpaired settings, where we pair disease images and maps when available. We quantitatively and subjectively, with a human study, evaluate the quality of pseudo-healthy images using several criteria. We show in a series of experiments, performed on ISLES, BraTS and Cam-CAN datasets, that our method is better than several baselines and methods from the literature. We also show that due to better training processes we could recover deformations, on surrounding tissue, caused by disease. Our implementation is publicly available at https://github.com/xiat0616/pseudo-healthy-synthesis.},
    author = {Tian Xia and Agisilaos Chartsias and Sotirios A. Tsaftaris},
    doi = {10.1016/j.media.2020.101719},
    issn = {13618423},
    journal = {Medical Image Analysis},
    keywords = {Generative adversarial networks,Pathology disentanglement,Pseudo-healthy synthesis},
    month = {8},
    pmid = {32540700},
    publisher = {Elsevier B.V.},
    title = {Pseudo-healthy synthesis with pathology disentanglement and adversarial learning},
    volume = {64},
    year = {2020},
 }
 @article{Bengio2013,
    abstract = {The success of machine learning algorithms generally depends on data representation, and we hypothesize that this is because different representations can entangle and hide more or less the different explanatory factors of variation behind the data. Although specific domain knowledge can be used to help design representations, learning with generic priors can also be used, and the quest for AI is motivating the design of more powerful representation-learning algorithms implementing such priors. This paper reviews recent work in the area of unsupervised feature learning and deep learning, covering advances in probabilistic models, autoencoders, manifold learning, and deep networks. This motivates longer term unanswered questions about the appropriate objectives for learning good representations, for computing representations (i.e., inference), and the geometrical connections between representation learning, density estimation, and manifold learning. © 1979-2012 IEEE.},
    author = {Yoshua Bengio and Aaron Courville and Pascal Vincent},
    doi = {10.1109/TPAMI.2013.50},
    issn = {01628828},
    issue = {8},
    journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
    keywords = {Boltzmann machine,Deep learning,autoencoder,feature learning,neural nets,representation learning,unsupervised learning},
   pages = {1798-1828},
    pmid = {23787338},
    title = {Representation learning: A review and new perspectives},
    volume = {35},
    year = {2013},
 }

 @article{Kobayashi2021,
    abstract = {In medical imaging, the characteristics purely derived from a disease should reflect the extent to which abnormal findings deviate from the normal features. Indeed, physicians often need corresponding images without abnormal findings of interest or, conversely, images that contain similar abnormal findings regardless of normal anatomical context. This is called comparative diagnostic reading of medical images, which is essential for a correct diagnosis. To support comparative diagnostic reading, content-based image retrieval (CBIR) that can selectively utilize normal and abnormal features in medical images as two separable semantic components will be useful. In this study, we propose a neural network architecture to decompose the semantic components of medical images into two latent codes: normal anatomy code and abnormal anatomy code. The normal anatomy code represents counterfactual normal anatomies that should have existed if the sample is healthy, whereas the abnormal anatomy code attributes to abnormal changes that reflect deviation from the normal baseline. By calculating the similarity based on either normal or abnormal anatomy codes or the combination of the two codes, our algorithm can retrieve images according to the selected semantic component from a dataset consisting of brain magnetic resonance images of gliomas. Moreover, it can utilize a synthetic query vector combining normal and abnormal anatomy codes from two different query images. To evaluate whether the retrieved images are acquired according to the targeted semantic component, the overlap of the ground-truth labels is calculated as metrics of the semantic consistency. Our algorithm provides a flexible CBIR framework by handling the decomposed features with qualitatively and quantitatively remarkable results.},
    author = {Kazuma Kobayashi and Ryuichiro Hataya and Yusuke Kurose and Mototaka Miyake and Masamichi Takahashi and Akiko Nakagawa and Tatsuya Harada and Ryuji Hamamoto},
    doi = {10.1016/j.media.2021.102227},
    issn = {13618423},
    journal = {Medical Image Analysis},
    keywords = {comparative diagnostic reading,content-based image retrieval,deep learning,disentangled representation,feature decomposition},
    month = {12},
    pmid = {34543911},
    publisher = {Elsevier B.V.},
    title = {Decomposing normal and abnormal features of medical images for content-based image retrieval of glioma imaging},
    volume = {74},
    year = {2021},
 }

 @misc{Higgins2018,
   doi = {10.48550/ARXIV.1812.02230},
     author = {Higgins, Irina and Amos, David and Pfau, David and Racaniere, Sebastien and Matthey, Loic and Rezende, Danilo and Lerchner, Alexander},
  
   keywords = {Machine Learning (cs.LG), Machine Learning (stat.ML), FOS: Computer and information sciences, FOS: Computer and information sciences},
  
   title = {Towards a Definition of Disentangled Representations},
  
   publisher = {arXiv},
  
   year = {2018},
  
   copyright = {arXiv.org perpetual, non-exclusive license}
 }

 @article{Gichoya2022,
    abstract = {Background: Previous studies in medical imaging have shown disparate abilities of artificial intelligence (AI) to detect a person's race, yet there is no known correlation for race on medical imaging that would be obvious to human experts when interpreting the images. We aimed to conduct a comprehensive evaluation of the ability of AI to recognise a patient's racial identity from medical images. Methods: Using private (Emory CXR, Emory Chest CT, Emory Cervical Spine, and Emory Mammogram) and public (MIMIC-CXR, CheXpert, National Lung Cancer Screening Trial, RSNA Pulmonary Embolism CT, and Digital Hand Atlas) datasets, we evaluated, first, performance quantification of deep learning models in detecting race from medical images, including the ability of these models to generalise to external environments and across multiple imaging modalities. Second, we assessed possible confounding of anatomic and phenotypic population features by assessing the ability of these hypothesised confounders to detect race in isolation using regression models, and by re-evaluating the deep learning models by testing them on datasets stratified by these hypothesised confounding variables. Last, by exploring the effect of image corruptions on model performance, we investigated the underlying mechanism by which AI models can recognise race. Findings: In our study, we show that standard AI deep learning models can be trained to predict race from medical images with high performance across multiple imaging modalities, which was sustained under external validation conditions (x-ray imaging [area under the receiver operating characteristics curve (AUC) range 0·91–0·99], CT chest imaging [0·87–0·96], and mammography [0·81]). We also showed that this detection is not due to proxies or imaging-related surrogate covariates for race (eg, performance of possible confounders: body-mass index [AUC 0·55], disease distribution [0·61], and breast density [0·61]). Finally, we provide evidence to show that the ability of AI deep learning models persisted over all anatomical regions and frequency spectrums of the images, suggesting the efforts to control this behaviour when it is undesirable will be challenging and demand further study. Interpretation: The results from our study emphasise that the ability of AI deep learning models to predict self-reported race is itself not the issue of importance. However, our finding that AI can accurately predict self-reported race, even from corrupted, cropped, and noised medical images, often when clinical experts cannot, creates an enormous risk for all model deployments in medical imaging. Funding: National Institute of Biomedical Imaging and Bioengineering, MIDRC grant of National Institutes of Health, US National Science Foundation, National Library of Medicine of the National Institutes of Health, and Taiwan Ministry of Science and Technology},
    author = {Judy Wawira Gichoya and Imon Banerjee and Ananth Reddy Bhimireddy and John L. Burns and Leo Anthony Celi and Li Ching Chen and Ramon Correa and Natalie Dullerud and Marzyeh Ghassemi and Shih Cheng Huang and Po Chih Kuo and Matthew P. Lungren and Lyle J. Palmer and Brandon J. Price and Saptarshi Purkayastha and Ayis T. Pyrros and Lauren Oakden-Rayner and Chima Okechukwu and Laleh Seyyed-Kalantari and Hari Trivedi and Ryan Wang and Zachary Zaiman and Haoran Zhang},
    doi = {10.1016/S2589-7500(22)00063-2},
    issn = {25897500},
    issue = {6},
    journal = {The Lancet Digital Health},
    month = {6},
    pages = {e406-e414},
    pmid = {35568690},
    publisher = {Elsevier Ltd},
    title = {AI recognition of patient race in medical imaging: a modelling study},
    volume = {4},
    year = {2022},
 }

 @article{Seastedt22,
     doi = {10.1371/journal.pdig.0000102},
     author = {Seastedt, Kenneth P. AND Schwab, Patrick AND O’Brien, Zach AND Wakida, Edith AND Herrera, Karen AND Marcelo, Portia Grace F. AND Agha-Mir-Salim, Louis AND Frigola, Xavier Borrat AND Ndulue, Emily Boardman AND Marcelo, Alvin AND Celi, Leo Anthony},
     journal = {PLOS Digital Health},
     publisher = {Public Library of Science},
     title = {Global healthcare fairness: We should be sharing more, not less, data},
     year = {2022},
     month = {10},
     volume = {1},
     pages = {1-13},
     abstract = {The availability of large, deidentified health datasets has enabled significant innovation in using machine learning (ML) to better understand patients and their diseases. However, questions remain regarding the true privacy of this data, patient control over their data, and how we regulate data sharing in a way that that does not encumber progress or further potentiate biases for underrepresented populations. After reviewing the literature on potential reidentifications of patients in publicly available datasets, we argue that the cost—measured in terms of access to future medical innovations and clinical software—of slowing ML progress is too great to limit sharing data through large publicly available databases for concerns of imperfect data anonymization. This cost is especially great for developing countries where the barriers preventing inclusion in such databases will continue to rise, further excluding these populations and increasing existing biases that favor high-income countries. Preventing artificial intelligence’s progress towards precision medicine and sliding back to clinical practice dogma may pose a larger threat than concerns of potential patient reidentification within publicly available datasets. While the risk to patient privacy should be minimized, we believe this risk will never be zero, and society has to determine an acceptable risk threshold below which data sharing can occur—for the benefit of a global medical knowledge system.},
     number = {10},

 }

@article{Aryanto2015,
   abstract = {Purpose: To compare non-commercial DICOM toolkits for their de-identification ability in removing a patient's personal health information (PHI) from a DICOM header. Materials and Methods: Ten DICOM toolkits were selected for de-identification tests. Tests were performed by using the system’s default de-identification profile and, subsequently, the tools' best adjusted settings. We aimed to eliminate fifty elements considered to contain identifying patient information. The tools were also examined for their respective methods of customization. Results: Only one tool was able to de-identify all required elements with the default setting. Not all of the toolkits provide a customizable de-identification profile. Six tools allowed changes by selecting the provided profiles, giving input through a graphical user interface (GUI) or configuration text file, or providing the appropriate command-line arguments. Using adjusted settings, four of those six toolkits were able to perform full de-identification. Conclusion: Only five tools could properly de-identify the defined DICOM elements, and in four cases, only after careful customization. Therefore, free DICOM toolkits should be used with extreme care to prevent the risk of disclosing PHI, especially when using the default configuration. In case optimal security is required, one of the five toolkits is proposed. Key Points: • Free DICOM toolkits should be carefully used to prevent patient identity disclosure. • Each DICOM tool produces its own specific outcomes from the de-identification process. • In case optimal security is required, using one DICOM toolkit is proposed.},
    author = {K. Y.E. Aryanto and M. Oudkerk and P. M.A. van Ooijen},
    doi = {10.1007/s00330-015-3794-0},
   issn = {14321084},
    issue = {12},
    journal = {European Radiology},
   keywords = {Anonymisation and Pseudonymisation,Anonymous Testing,DICOM Freeware Tools,Data Protection,Patient Data Privacy},
    month = {12},
    pages = {3685-3695},
    pmid = {26037716},
    publisher = {Springer Verlag},
    title = {Free DICOM de-identification tools in clinical research: functioning and safety of patient privacy},
    volume = {25},
    year = {2015},
 }
 @article{Willemink2020,
    abstract = {Artificial intelligence (AI) continues to garner substantial interest in medical imaging. The potential applications are vast and include the entirety of the medical imaging life cycle from image creation to diagnosis to outcome prediction. The chief obstacles to development and clinical implementation of AI algorithms include availability of sufficiently large, curated, and representative training data that includes expert labeling (eg, annotations). Current supervised AI methods require a curation process for data to optimally train, validate, and test algorithms. Currently, most research groups and industry have limited data access based on small sample sizes from small geographic areas. In addition, the preparation of data is a costly and time-intensive process, the results of which are algorithms with limited utility and poor generalization. In this article, the authors describe fundamental steps for preparing medical imaging data in AI algorithm development, explain current limitations to data curation, and explore new approaches to address the problem of data availability.},
    author = {Martin J. Willemink and Wojciech A. Koszek and Cailin Hardell and Jie Wu and Dominik Fleischmann and Hugh Harvey and Les R. Folio and Ronald M. Summers and Daniel L. Rubin and Matthew P. Lungren},
    doi = {10.1148/radiol.2020192224},
    issn = {15271315},
    issue = {1},
    journal = {Radiology},
    pages = {4-15},
    pmid = {32068507},
    publisher = {Radiological Society of North America Inc.},
    title = {Preparing medical imaging data for machine learning},
    volume = {295},
    year = {2020},
 }

 @article{kose2011,
    author = {Cemal Köse and Cevat Ikibaşs},
    doi = {10.1016/j.eswa.2011.04.141},
    issn = {09574174},
    issue = {11},
    journal = {Expert Systems with Applications},
    keywords = {Biometric identification,Retinal identification,Retinal image analysis,Similarity measurement,Vessel segmentation},
    month = {10},
    pages = {13670-13681},
    title = {A personal identification system using retinal vasculature in retinal fundus images},
    volume = {38},
    year = {2011},
 }

 @article{Nomir2007,
    abstract = {Dental biometrics deal with human identification from dental characteristics. In this paper, we present a new technique for identifying people based upon shapes and appearances of their teeth from dental X-ray radiographs. The new technique represents each tooth by a feature vector obtained from the forcefield energy function of the grayscale image of the tooth and Fourier descriptors of the contour of the tooth. The feature vector is composed of the distances between a small number of potential energy wells as well as a small number of Fourier descriptors. Given a query image (i.e., postmortem radiograph), each tooth is matched with the archived teeth in the database (antemortem radiographs) that have the same tooth number. Then, voting is used to obtain a list of best matches for the query image based upon the matching results of the individual teeth. Our goal of using appearance and shape-based features together is to overcome the drawback of using only the contour of the tooth, which can be strongly affected by the quality of the images. The experimental results on a database of 162 antemortem images show that our method is effective in identifying individuals based on their dental radiographs. © 2007 IEEE.},
    author = {Omaima Nomir and Mohamed Abdel-Mottaleb},
    doi = {10.1109/TIFS.2007.897245},
    issn = {15566013},
    issue = {2},
    journal = {IEEE Transactions on Information Forensics and Security},
    keywords = {Antemortem (AM) radiographs,Biometrics,Dental images,Forcefield,Forensic odontology,Fourier descriptors,Human identification,Image segmentation,Postmortem (PM) radiographs},
    month = {6},
    pages = {188-197},
    title = {Human identification from dental X-ray images based on the shape and appearance of the teeth},
    volume = {2},
    year = {2007},
 }

 @article{Wachinger2015,
    abstract = {We introduce BrainPrint, a compact and discriminative representation of brain morphology. BrainPrint captures shape information of an ensemble of cortical and subcortical structures by solving the eigenvalue problem of the 2D and 3D Laplace-Beltrami operator on triangular (boundary) and tetrahedral (volumetric) meshes. This discriminative characterization enables new ways to study the similarity between brains; the focus can either be on a specific brain structure of interest or on the overall brain similarity. We highlight four applications for BrainPrint in this article: (i) subject identification, (ii) age and sex prediction, (iii) brain asymmetry analysis, and (iv) potential genetic influences on brain morphology. The properties of BrainPrint require the derivation of new algorithms to account for the heterogeneous mix of brain structures with varying discriminative power. We conduct experiments on three datasets, including over 3000 MRI scans from the ADNI database, 436 MRI scans from the OASIS dataset, and 236 MRI scans from the VETSA twin study. All processing steps for obtaining the compact representation are fully automated, making this processing framework particularly attractive for handling large datasets.},
    author = {Christian Wachinger and Polina Golland and William Kremen and Bruce Fischl and Martin Reuter},
    doi = {10.1016/j.neuroimage.2015.01.032},
    issn = {10959572},
    journal = {NeuroImage},
    keywords = {Brain asymmetry,Brain shape,Brain similarity,Large brain datasets,Morphological heritability,Subject identification},
    month = {4},
    pages = {232-248},
    pmid = {25613439},
    publisher = {Academic Press Inc.},
    title = {BrainPrint: A discriminative characterization of brain morphology},
    volume = {109},
    year = {2015},
 }

 @article{Kabbara2013,
    author = {Yeihya Kabbara and Ahmad Shahin and Amine Nait-Ali and Mohamad Khalil},
    doi = {10.1109/ICABME.2013.6648874},
    isbn = {9781479902514},
    journal = {2013 2nd International Conference on Advances in Biomedical Engineering, ICABME 2013},
    keywords = {Forgeries,Fourier Transform,Hand X-ray,Hidden Biometrics,Human Identification,Segmentation,Spoofing},
    month = {10},
    pages = {167-170},
    publisher = {Institute of Electrical and Electronics Engineers Inc.},
    title = {An automatic algorithm for human identification using hand X-ray images},
    year = {2013},
 }

 @article{Shamir2009,
    author = {Lior Shamir and Shari Ling and Salim Rahimi and Luigi Ferrucci and Ilya G. Goldberg},
    doi = {10.1504/IJBM.2009.024279},
    issn = {1755831X},
    issue = {3},
    journal = {International Journal of Biometrics},
    keywords = {Biometrics,Bone,Joint,Radiography,X-ray},
    pages = {365-370},
    publisher = {Inderscience Publishers},
    title = {Biometric identification using knee X-rays},
    volume = {1},
    year = {2009},
 }

@inproceedings{Madras2018,
  title={Learning adversarially fair and transferable representations},
  author={Madras, David and Creager, Elliot and Pitassi, Toniann and Zemel, Richard},
  booktitle={International Conference on Machine Learning},
  pages={3384--3393},
  year={2018},
  organization={PMLR}
}


 @inproceedings{
 Singla2020Explanation,
 title={Explanation  by Progressive  Exaggeration},
 author={Sumedha Singla and Brian Pollack and Junxiang Chen and Kayhan Batmanghelich},
 booktitle={International Conference on Learning Representations},
 year={2020},
 }

 @InProceedings{explaining_in_style,
     author    = {Lang, Oran and Gandelsman, Yossi and Yarom, Michal and Wald, Yoav and Elidan, Gal and Hassidim, Avinatan and Freeman, William T. and Isola, Phillip and Globerson, Amir and Irani, Michal and Mosseri, Inbar},
     title     = {Explaining in Style: Training a GAN To Explain a Classifier in StyleSpace},
     booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
     month     = {October},
     year      = {2021},
     pages     = {693-702}
 }


 @article{Diamant22,
     doi = {10.1371/journal.pcbi.1009862},
     author = {Diamant, Nathaniel AND Reinertsen, Erik AND Song, Steven AND Aguirre, Aaron D. AND Stultz, Collin M. AND Batra, Puneet},
     journal = {PLOS Computational Biology},
     publisher = {Public Library of Science},
     title = {Patient contrastive learning: A performant, expressive, and practical approach to electrocardiogram modeling},
     year = {2022},
     month = {02},
     volume = {18},
     pages = {1-16},
     abstract = {Supervised machine learning applications in health care are often limited due to a scarcity of labeled training data. To mitigate the effect of small sample size, we introduce a pre-training approach, Patient Contrastive Learning of Representations (PCLR), which creates latent representations of electrocardiograms (ECGs) from a large number of unlabeled examples using contrastive learning. The resulting representations are expressive, performant, and practical across a wide spectrum of clinical tasks. We develop PCLR using a large health care system with over 3.2 million 12-lead ECGs and demonstrate that training linear models on PCLR representations achieves a 51
     },
     number = {2},

 }

@article{physiobank00,
author = {Ary L. Goldberger  and Luis A. N. Amaral  and Leon Glass  and Jeffrey M. Hausdorff  and Plamen Ch. Ivanov  and Roger G. Mark  and Joseph E. Mietus  and George B. Moody  and Chung-Kang Peng  and H. Eugene Stanley },
title = {PhysioBank, PhysioToolkit, and PhysioNet  },
journal = {Circulation},
volume = {101},
number = {23},
pages = {e215-e220},
year = {2000},
doi = {10.1161/01.CIR.101.23.e215},
 }


@article{sellergren2022,
  title={Simplified transfer learning for chest radiography models using less data},
  author={Sellergren, Andrew B and Chen, Christina and Nabulsi, Zaid and Li, Yuanzhen and Maschinot, Aaron and Sarna, Aaron and Huang, Jenny and Lau, Charles and Kalidindi, Sreenivasa Raju and Etemadi, Mozziyar and others},
  journal={Radiology},
  volume={305},
  number={2},
  pages={454--465},
  year={2022},
  publisher={Radiological Society of North America}
}

@article{cid2023development,
  title={Development and validation of open-source deep neural networks for comprehensive chest x-ray reading: a retrospective, multicentre study},
  author={Dicente Cid, Yashin and Macpherson, Matthew and Gervais-Andre, Louise and Zhu, Yuanyi and Franco, Giuseppe and Santeramo, Ruggiero and Lim, Chee and Selby, Ian and Muthuswamy, Keerthini and Amlani, Ashik and others},
  journal={The Lancet Digital Health},
  year={2023},
  publisher={Elsevier}
}

@article{zhu2020deep,
  title={Deep transfer learning artificial intelligence accurately stages COVID-19 lung disease severity on portable chest radiographs},
  author={Zhu, Jocelyn and Shen, Beiyi and Abbasi, Almas and Hoshmand-Kochi, Mahsa and Li, Haifang and Duong, Tim Q},
  journal={PloS one},
  volume={15},
  number={7},
  pages={e0236621},
  year={2020},
  publisher={Public Library of Science San Francisco, CA USA}
}

@article{signoroni2021bs,
  title={BS-Net: Learning COVID-19 pneumonia severity on a large chest X-ray dataset},
  author={Signoroni, Alberto and Savardi, Mattia and Benini, Sergio and Adami, Nicola and Leonardi, Riccardo and Gibellini, Paolo and Vaccher, Filippo and Ravanelli, Marco and Borghesi, Andrea and Maroldi, Roberto and others},
  journal={Medical Image Analysis},
  volume={71},
  pages={102046},
  year={2021},
  publisher={Elsevier}
}

@article{chandra2022disease,
  title={Disease localization and severity assessment in chest X-ray images using multi-stage superpixels classification},
  author={Chandra, Tej Bahadur and Singh, Bikesh Kumar and Jain, Deepak},
  journal={Computer Methods and Programs in Biomedicine},
  volume={222},
  pages={106947},
  year={2022},
  publisher={Elsevier}
}

@inproceedings{NIPS2006_af44c4c5,
 author = {Burges, Christopher and Ragno, Robert and Le, Quoc},
 booktitle = {Advances in Neural Information Processing Systems},
 editor = {B. Sch\"{o}lkopf and J. Platt and T. Hoffman},
 pages = {},
 publisher = {MIT Press},
 title = {Learning to Rank with Nonsmooth Cost Functions},
 volume = {19},
 year = {2006}
}

@article{zhang2021development,
  title={Development and multicenter validation of chest X-ray radiography interpretations based on natural language processing},
  author={Zhang, Yaping and Liu, Mingqian and Hu, Shundong and Shen, Yao and Lan, Jun and Jiang, Beibei and de Bock, Geertruida H and Vliegenthart, Rozemarijn and Chen, Xu and Xie, Xueqian},
  journal={Communications medicine},
  volume={1},
  number={1},
  pages={43},
  year={2021},
  publisher={Nature Publishing Group UK London}
}

@inproceedings{cornegruta-etal-2016-modelling,
    title = "Modelling Radiological Language with Bidirectional Long Short-Term Memory Networks",
    author = "Cornegruta, Savelie  and
      Bakewell, Robert  and
      Withey, Samuel  and
      Montana, Giovanni",
    editor = "Grouin, Cyril  and
      Hamon, Thierry  and
      N{\'e}v{\'e}ol, Aur{\'e}lie  and
      Zweigenbaum, Pierre",
    booktitle = "Proceedings of the Seventh International Workshop on Health Text Mining and Information Analysis",
    month = nov,
    year = "2016",
    address = "Auxtin, TX",
    publisher = "Association for Computational Linguistics",
    doi = "10.18653/v1/W16-6103",
    pages = "17--27",
}