% Long form of conference & journal abbreviations -- especially for camera ready
@String(PAMI  = {IEEE Trans. Pattern Anal. Mach. Intell.})
@String(IJCV  = {Int. J. Comput. Vis.})
@String(CVPR  = {IEEE Conf. Comput. Vis. Pattern Recog.})
@String(ICCV  = {Int. Conf. Comput. Vis.})
@String(ECCV  = {Eur. Conf. Comput. Vis.})
@String(NeurIPS = {Adv. Neural Inform. Process. Syst.})
@String(ICML  = {Int. Conf. Mach. Learn.})
@String(ICLR  = {Int. Conf. Learn. Represent.})
@String(ACCV  = {Asian Conf. Comput. Vis.})
@String(BMVC  = {Brit. Mach. Vis. Conf.})
@String(CVPRW = {IEEE Conf. Comput. Vis. Pattern Recog. Worksh.})
@String(AAAI  = {AAAI})
@String(IJCAI = {IJCAI})
@String(ICIP  = {IEEE Int. Conf. Image Process.})
@String(ICPR  = {Int. Conf. Pattern Recog.})
@String(ICASSP=	{ICASSP})
@String(ICME  = {Int. Conf. Multimedia and Expo})
@String(JMLR  = {J. Mach. Learn. Res.})
@String(TMLR  = {Trans. Mach. Learn Res.})
@String(TOG   = {ACM Trans. Graph.})
@String(TIP   = {IEEE Trans. Image Process.})
@String(TVCG  = {IEEE Trans. Vis. Comput. Graph.})
@String(TCSVT = {IEEE Trans. Circuit Syst. Video Technol.})
@String(TMM   = {IEEE Trans. Multimedia})
@String(ACMMM = {ACM Int. Conf. Multimedia})
@String(PR    = {Pattern Recognition})

@String(MNI	  = {Nature Mach. Intell.})
@String(SPL	  = {IEEE Sign. Process. Letters})
@String(VR    = {Vis. Res.})
@String(JOV	  = {J. Vis.})
@String(TVC   = {The Vis. Comput.})
@String(JCST  = {J. Comput. Sci. Tech.})
@String(CGF   = {Comput. Graph. Forum})
@String(CVM   = {Computational Visual Media})


% Short form of conference & journal abbreviations -- especially for submission version
% if desired, remove these macros in favor of the above ones
@String(PAMI  = {IEEE TPAMI})
@String(IJCV  = {IJCV})
@String(CVPR  = {CVPR})
@String(ICCV  = {ICCV})
@String(ECCV  = {ECCV})
@String(NeurIPS = {NeurIPS})
@String(ICML  = {ICML})
@String(ICLR  = {ICLR})
@String(ACCV  = {ACCV})
@String(BMVC  =	{BMVC})
@String(CVPRW = {CVPRW})
@String(AAAI  = {AAAI})
@String(IJCAI = {IJCAI})
@String(ICIP  = {ICIP})
@String(ICPR  = {ICPR})
@String(ICASSP=	{ICASSP})
@String(ICME  =	{ICME})
@String(JMLR  = {JMLR})
@String(TMLR  = {TMLR})
@String(TOG   = {ACM TOG})
@String(TIP   = {IEEE TIP})
@String(TVCG  = {IEEE TVCG})
@String(TCSVT = {IEEE TCSVT})
@String(TMM   =	{IEEE TMM})
@String(ACMMM = {ACM MM})
@String(PR    = {PR})



@misc{Anonymous24,
 author = {Anonymous},
 title = {The frobnicatable foo filter},
 note = {{ECCV} submission ID 00324, supplied as supplemental material {\tt 00324.pdf}},
 year = 2024
}

@misc{Anonymous24b,
 author = {Anonymous},
 title = {Frobnication tutorial},
 note = {Supplied as supplemental material {\tt tr.pdf}},
 year = 2024
}

@article{Alpher02,
author = {FirstName Alpher},
title = {Frobnication},
journal = PAMI,
volume = 12,
number = 1,
pages = {234--778},
year = 2002
}

@article{Alpher03,
author = {FirstName Alpher and FirstName Fotheringham-Smythe},
title = {Frobnication revisited},
journal = {Journal of Foo},
volume = 13,
number = 1,
pages = {234--778},
year = 2003
}

@article{Alpher04,
author = {FirstName Alpher and FirstName Fotheringham-Smythe and FirstName Gamow},
title = {Can a machine frobnicate?},
journal = {Journal of Foo},
volume = 14,
number = 1,
pages = {234--778},
year = 2004
}

@inproceedings{Alpher05,
author = {FirstName Alpher and FirstName Gamow},
title = {Can a computer frobnicate?},
booktitle = CVPR,
pages = {234--778},
year = 2005
}

@book{ECCV2022,
    editor = {Shai Avidan and Gabriel Brostow and Moustapha Cissé and Giovanni Maria Farinella and Tal Hassner},
    title = {Computer Vision -- ECCV 2022},
    year = {2022},
    publisher = {Springer},
    doi = {10.1007/978-3-031-19769-7}
}

@String(PAMI = {IEEE Trans. Pattern Anal. Mach. Intell.})
@String(IJCV = {Int. J. Comput. Vis.})
@String(CVPR= {IEEE Conf. Comput. Vis. Pattern Recog.})
@String(ICCV= {Int. Conf. Comput. Vis.})
@String(ECCV= {Eur. Conf. Comput. Vis.})
@String(NIPS= {Adv. Neural Inform. Process. Syst.})
@String(ICPR = {Int. Conf. Pattern Recog.})
@String(BMVC= {Brit. Mach. Vis. Conf.})
@String(TOG= {ACM Trans. Graph.})
@String(TIP  = {IEEE Trans. Image Process.})
@String(TVCG  = {IEEE Trans. Vis. Comput. Graph.})
@String(TMM  = {IEEE Trans. Multimedia})
@String(ACMMM= {ACM Int. Conf. Multimedia})
@String(ICME = {Int. Conf. Multimedia and Expo})
@String(ICASSP=	{ICASSP})
@String(ICIP = {IEEE Int. Conf. Image Process.})
@String(ACCV  = {ACCV})
@String(ICLR = {Int. Conf. Learn. Represent.})
@String(IJCAI = {IJCAI})
@String(PR   = {Pattern Recognition})
@String(AAAI = {AAAI})
@String(CVPRW= {IEEE Conf. Comput. Vis. Pattern Recog. Worksh.})
@String(CSVT = {IEEE Trans. Circuit Syst. Video Technol.})

@String(SPL	= {IEEE Sign. Process. Letters})
@String(VR   = {Vis. Res.})
@String(JOV	 = {J. Vis.})
@String(TVC  = {The Vis. Comput.})
@String(JCST  = {J. Comput. Sci. Tech.})
@String(CGF  = {Comput. Graph. Forum})
@String(CVM = {Computational Visual Media})


@String(PAMI  = {IEEE TPAMI})
@String(IJCV  = {IJCV})
@String(CVPR  = {CVPR})
@String(ICCV  = {ICCV})
@String(ECCV  = {ECCV})
@String(NIPS  = {NeurIPS})
@String(ICPR  = {ICPR})
@String(BMVC  =	{BMVC})
@String(TOG   = {ACM TOG})
@String(TIP   = {IEEE TIP})
@String(TVCG  = {IEEE TVCG})
@String(TCSVT = {IEEE TCSVT})
@String(TMM   =	{IEEE TMM})
@String(ACMMM = {ACM MM})
@String(ICME  =	{ICME})
@String(ICASSP=	{ICASSP})
@String(ICIP  = {ICIP})
@String(ACCV  = {ACCV})
@String(ICLR  = {ICLR})
@String(IJCAI = {IJCAI})
@String(PR = {PR})
@String(AAAI = {AAAI})
@String(CVPRW= {CVPRW})
@String(CSVT = {IEEE TCSVT})


@article{yang2022introduction,
  title={An introduction to neural data compression},
  author={Yang, Yibo and Mandt, Stephan and Theis, Lucas},
  journal={arXiv preprint arXiv:2202.06533},
  year={2022}
}

@article{dupont2022coin++,
  title={Coin++: Data agnostic neural compression},
  author={Dupont, Emilien and Loya, Hrushikesh and Alizadeh, Milad and Goli{\'n}ski, Adam and Teh, Yee Whye and Doucet, Arnaud},
  journal={arXiv preprint arXiv:2201.12904},
  year={2022}
}

@inproceedings{sushmit2019x,
  title={X-ray image compression using convolutional recurrent neural networks},
  author={Sushmit, Asif Shahriyar and Zaman, Shakib Uz and Humayun, Ahmed Imtiaz and Hasan, Taufiq and Bhuiyan, Mohammed Imamul Hassan},
  booktitle={2019 IEEE EMBS International Conference on Biomedical \& Health Informatics (BHI)},
  pages={1--4},
  year={2019},
  organization={IEEE}
}

@article{panagiotidis1996neural,
  title={Neural network-assisted effective lossy compression of medical images},
  author={Panagiotidis, Nikos G and Kalogeras, Dimitris and Kollias, Stefanos D and Stafylopatis, Andreas},
  journal={Proceedings of the IEEE},
  volume={84},
  number={10},
  pages={1474--1487},
  year={1996},
  publisher={IEEE}
}

@article{goodfellow2020generative,
  title={Generative adversarial networks},
  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={Communications of the ACM},
  volume={63},
  number={11},
  pages={139--144},
  year={2020},
  publisher={ACM New York, NY, USA}
}

@article{balle2016end,
  title={End-to-end optimized image compression},
  author={Ball{\'e}, Johannes and Laparra, Valero and Simoncelli, Eero P},
  journal={arXiv preprint arXiv:1611.01704},
  year={2016}
}

@article{theis2017lossy,
  title={Lossy image compression with compressive autoencoders},
  author={Theis, Lucas and Shi, Wenzhe and Cunningham, Andrew and Husz{\'a}r, Ferenc},
  journal={arXiv preprint arXiv:1703.00395},
  year={2017}
}

@inproceedings{zhou2018variational,
  title={Variational autoencoder for low bit-rate image compression},
  author={Zhou, Lei and Cai, Chunlei and Gao, Yue and Su, Sanbao and Wu, Junmin},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops},
  pages={2617--2620},
  year={2018}
}

@article{van2023exploring,
  title={Exploring Image Augmentations for Siamese Representation Learning with Chest X-Rays},
  author={Van der Sluijs, Rogier and Bhaskhar, Nandita and Rubin, Daniel and Langlotz, Curtis and Chaudhari, Akshay},
  journal={arXiv preprint arXiv:2301.12636},
  year={2023}
}

@misc{compressioncc,
    title = {CLIC 2022, the 5th Workshop and Challenge on Learned Image Compression},
    howpublished = {\url{https://compression.cc/}}
}

@article{tellez2019neural,
  title={Neural image compression for gigapixel histopathology image analysis},
  author={Tellez, David and Litjens, Geert and van der Laak, Jeroen and Ciompi, Francesco},
  journal={IEEE transactions on pattern analysis and machine intelligence},
  volume={43},
  number={2},
  pages={567--578},
  year={2019},
  publisher={IEEE}
}

@inproceedings{aswolinskiy2021neural,
  title={Neural image compression for non-small cell lung cancer subtype classification in H\&E stained whole-slide images},
  author={Aswolinskiy, Witali and Tellez, David and Raya, Gabriel and van der Woude, Lieke and Looijen-Salamon, Monika and van der Laak, Jeroen and Grunberg, Katrien and Ciompi, Francesco},
  booktitle={Medical Imaging 2021: Digital Pathology},
  volume={11603},
  pages={1160304},
  year={2021},
  organization={SPIE}
}

@inproceedings{tellez2020extending,
  title={Extending unsupervised neural image compression with supervised multitask learning},
  author={Tellez, David and H{\"o}ppener, Diederik and Verhoef, Cornelis and Gr{\"u}nhagen, Dirk and Nierop, Pieter and Drozdzal, Michal and Laak, Jeroen and Ciompi, Francesco},
  booktitle={Medical Imaging with Deep Learning},
  pages={770--783},
  year={2020},
  organization={PMLR}
}

@inproceedings{rombach2022high,
  title={High-resolution image synthesis with latent diffusion models},
  author={Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj{\"o}rn},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={10684--10695},
  year={2022}
}

@inproceedings{esser2021taming,
  title={Taming transformers for high-resolution image synthesis},
  author={Esser, Patrick and Rombach, Robin and Ommer, Bjorn},
  booktitle={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition},
  pages={12873--12883},
  year={2021}
}

@article{dai2019diagnosing,
  title={Diagnosing and enhancing VAE models},
  author={Dai, Bin and Wipf, David},
  journal={arXiv preprint arXiv:1903.05789},
  year={2019}
}

% Datasets
% Emory
@article{jeong2022emory,
  title={The emory breast imaging dataset (embed): A racially diverse, granular dataset of 3.5 m screening and diagnostic mammograms},
  author={Jeong, Jiwoong J and Vey, Brianna L and Reddy, Ananth and Kim, Thomas and Santos, Thiago and Correa, Ramon and Dutt, Raman and Mosunjac, Marina and Oprea-Ilies, Gabriela and Smith, Geoffrey and others},
  journal={arXiv preprint arXiv:2202.04073},
  year={2022}
}

% CBIS-DDSM (segmentation labels) -- scanned film mammography
@article{lee2017curated,
  title={A curated mammography data set for use in computer-aided detection and diagnosis research},
  author={Lee, Rebecca Sawyer and Gimenez, Francisco and Hoogi, Assaf and Miyake, Kanae Kawai and Gorovoy, Mia and Rubin, Daniel L},
  journal={Scientific data},
  volume={4},
  number={1},
  pages={1--9},
  year={2017},
  publisher={Nature Publishing Group}
}

@article{moreira2012inbreast,
  title={Inbreast: toward a full-field digital mammographic database},
  author={Moreira, In{\^e}s C and Amaral, Igor and Domingues, In{\^e}s and Cardoso, Ant{\'o}nio and Cardoso, Maria Joao and Cardoso, Jaime S},
  journal={Academic radiology},
  volume={19},
  number={2},
  pages={236--248},
  year={2012},
  publisher={Elsevier}
}

@article{kuznetsova2020open,
  title={The open images dataset v4: Unified image classification, object detection, and visual relationship detection at scale},
  author={Kuznetsova, Alina and Rom, Hassan and Alldrin, Neil and Uijlings, Jasper and Krasin, Ivan and Pont-Tuset, Jordi and Kamali, Shahab and Popov, Stefan and Malloci, Matteo and Kolesnikov, Alexander and others},
  journal={International Journal of Computer Vision},
  volume={128},
  number={7},
  pages={1956--1981},
  year={2020},
  publisher={Springer}
}

@article{feng2021curation,
  title={Curation of the candid-ptx dataset with free-text reports},
  author={Feng, Sijing and Azzollini, Damian and Kim, Ji Soo and Jin, Cheng-Kai and Gordon, Simon P and Yeoh, Jason and Kim, Eve and Han, Mina and Lee, Andrew and Patel, Aakash and others},
  journal={Radiology: Artificial Intelligence},
  volume={3},
  number={6},
  pages={e210136},
  year={2021},
  publisher={Radiological Society of North America}
}

@article{sorkhei2021csaw,
  title={Csaw-M: An ordinal classification dataset for benchmarking mammographic masking of cancer},
  author={Sorkhei, Moein and others},
  journal={arXiv preprint arXiv:2112.01330},
  year={2021}
}

@article{nguyen2022vindrmammo,
  title={VinDr-Mammo: A large-scale benchmark dataset for computer-aided diagnosis in full-field digital mammography},
  author={Nguyen, Hieu Trung and Nguyen, Ha Quy and Pham, Hieu Huy and Lam, Khanh and Le, Linh Tuan and Dao, Minh and Vu, Van},
  journal={MedRxiv},
  pages={2022--03},
  year={2022},
  publisher={Cold Spring Harbor Laboratory Press}
}

@article{jeong2023emory,
  title={The EMory BrEast imaging Dataset (EMBED): A racially diverse, granular dataset of 3.4 million screening and diagnostic mammographic images},
  author={Jeong, Jiwoong J and Vey, Brianna L and Bhimireddy, Ananth and Kim, Thomas and Santos, Thiago and Correa, Ramon and Dutt, Raman and Mosunjac, Marina and Oprea-Ilies, Gabriela and Smith, Geoffrey and others},
  journal={Radiology: Artificial Intelligence},
  volume={5},
  number={1},
  pages={e220047},
  year={2023},
  publisher={Radiological Society of North America}
}

@article{nguyen2022vindrcxr,
  title={VinDr-CXR: An open dataset of chest X-rays with radiologist’s annotations},
  author={Nguyen, Ha Q and Lam, Khanh and Le, Linh T and Pham, Hieu H and Tran, Dat Q and Nguyen, Dung B and Le, Dung D and Pham, Chi M and Tong, Hang TT and Dinh, Diep H and others},
  journal={Scientific Data},
  volume={9},
  number={1},
  pages={429},
  year={2022},
  publisher={Nature Publishing Group UK London}
}

@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}
}

@article{cai2023online,
  title={An Online Mammography Database with Biopsy Confirmed Types},
  author={Cai, Hongmin and Wang, Jinhua and Dan, Tingting and Li, Jiao and Fan, Zhihao and Yi, Weiting and Cui, Chunyan and Jiang, Xinhua and Li, Li},
  journal={Scientific Data},
  volume={10},
  number={1},
  pages={123},
  year={2023},
  publisher={Nature Publishing Group UK London}
}

@article{openimages,
  title={OpenImages: A public dataset for large-scale multi-label and multi-class image classification.},
  author={Krasin, Ivan and Duerig, Tom and Alldrin, Neil and Ferrari, Vittorio and Abu-El-Haija, Sami and Kuznetsova, Alina and Rom, Hassan and Uijlings, Jasper and Popov, Stefan and Veit, Andreas and Belongie, Serge and Gomes, Victor and Gupta, Abhinav and Sun, Chen and Chechik, Gal and Cai, David and Feng, Zheyun and Narayanan, Dhyanesh and Murphy, Kevin},
  journal={Dataset available from https://github.com/openimages},
  year={2017}
}

@article{chambon2022roentgen,
  title={{RoentGen: Vision-Language Foundation Model for Chest X-ray Generation}},
  author={Chambon, Pierre and Bluethgen, Christian and Delbrouck, Jean-Benoit and Van der Sluijs, Rogier and Po{\l}acin, Ma{\l}gorzata and Chaves, Juan Manuel Zambrano and Abraham, Tanishq Mathew and Purohit, Shivanshu and Langlotz, Curtis P and Chaudhari, Akshay},
  journal={arXiv preprint arXiv:2211.12737},
  year={2022}
}

% 22347940 
@Article{europeanrad2011,
   Author="European Society of Radiology",
   Title="{{U}sability of irreversible image compression in radiological imaging. {A} position paper by the {E}uropean {S}ociety of {R}adiology ({E}{S}{R})}",
   Journal="Insights Imaging",
   Year="2011",
   Volume="2",
   Number="2",
   Pages="103--115",
   Month="Apr"
}

@article{huda2015resolution,
  title={X-ray-based medical imaging and resolution},
  author={Huda, Walter and Abrahams, R Brad},
  journal={American Journal of Roentgenology},
  volume={204},
  number={4},
  pages={W393--W397},
  year={2015},
  publisher={Am Roentgen Ray Soc}
}

@article{freire2022computational,
  title={Computational complexity evaluation of neural network applications in signal processing},
  author={Freire, Pedro J and Srivallapanondh, Sasipim and Napoli, Antonio and Prilepsky, Jaroslaw E and Turitsyn, Sergei K},
  journal={arXiv preprint arXiv:2206.12191},
  year={2022}
}

@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{wallace1992jpeg,
  title={The JPEG still picture compression standard},
  author={Wallace, Gregory K},
  journal={IEEE transactions on consumer electronics},
  volume={38},
  number={1},
  pages={xviii--xxxiv},
  year={1992},
  publisher={IEEE}
}

@inproceedings{dodge2016understanding,
  title={Understanding how image quality affects deep neural networks},
  author={Dodge, Samuel and Karam, Lina},
  booktitle={2016 eighth international conference on quality of multimedia experience (QoMEX)},
  pages={1--6},
  year={2016},
  organization={IEEE}
}

@article{wang2020hrnet,
  title={Deep high-resolution representation learning for visual recognition},
  author={Wang, Jingdong and others},
  journal={IEEE transactions on pattern analysis and machine intelligence},
  volume={43},
  number={10},
  pages={3349--3364},
  year={2020},
  publisher={IEEE}
}


@article{feng2021candid,
  title={Curation of the candid-ptx dataset with free-text reports},
  author={Feng, Sijing and Azzollini, Damian and Kim, Ji Soo and Jin, Cheng-Kai and Gordon, Simon P and Yeoh, Jason and Kim, Eve and Han, Mina and Lee, Andrew and Patel, Aakash and others},
  journal={Radiology: Artificial Intelligence},
  volume={3},
  number={6},
  pages={e210136},
  year={2021},
  publisher={Radiological Society of North America}
}

@article{yang2022introduction,
  title={An introduction to neural data compression},
  author={Yang, Yibo and Mandt, Stephan and Theis, Lucas},
  journal={arXiv preprint arXiv:2202.06533},
  year={2022}
}

@article{dupont2022coin++,
  title={Coin++: Data agnostic neural compression},
  author={Dupont, Emilien and Loya, Hrushikesh and Alizadeh, Milad and Goli{\'n}ski, Adam and Teh, Yee Whye and Doucet, Arnaud},
  journal={arXiv preprint arXiv:2201.12904},
  year={2022}
}

@inproceedings{sushmit2019x,
  title={X-ray image compression using convolutional recurrent neural networks},
  author={Sushmit, Asif Shahriyar and Zaman, Shakib Uz and Humayun, Ahmed Imtiaz and Hasan, Taufiq and Bhuiyan, Mohammed Imamul Hassan},
  booktitle={2019 IEEE EMBS International Conference on Biomedical \& Health Informatics (BHI)},
  pages={1--4},
  year={2019},
  organization={IEEE}
}

@article{panagiotidis1996neural,
  title={Neural network-assisted effective lossy compression of medical images},
  author={Panagiotidis, Nikos G and Kalogeras, Dimitris and Kollias, Stefanos D and Stafylopatis, Andreas},
  journal={Proceedings of the IEEE},
  volume={84},
  number={10},
  pages={1474--1487},
  year={1996},
  publisher={IEEE}
}

@article{tellez2019neural,
  title={Neural image compression for gigapixel histopathology image analysis},
  author={Tellez, David and Litjens, Geert and van der Laak, Jeroen and Ciompi, Francesco},
  journal={IEEE transactions on pattern analysis and machine intelligence},
  volume={43},
  number={2},
  pages={567--578},
  year={2019},
  publisher={IEEE}
}

@inproceedings{aswolinskiy2021neural,
  title={Neural image compression for non-small cell lung cancer subtype classification in H\&E stained whole-slide images},
  author={Aswolinskiy, Witali and Tellez, David and Raya, Gabriel and van der Woude, Lieke and Looijen-Salamon, Monika and van der Laak, Jeroen and Grunberg, Katrien and Ciompi, Francesco},
  booktitle={Medical Imaging 2021: Digital Pathology},
  volume={11603},
  pages={1160304},
  year={2021},
  organization={SPIE}
}

@inproceedings{tellez2020extending,
  title={Extending unsupervised neural image compression with supervised multitask learning},
  author={Tellez, David and H{\"o}ppener, Diederik and Verhoef, Cornelis and Gr{\"u}nhagen, Dirk and Nierop, Pieter and Drozdzal, Michal and Laak, Jeroen and Ciompi, Francesco},
  booktitle={Medical Imaging with Deep Learning},
  pages={770--783},
  year={2020},
  organization={PMLR}
}

@article{dai2019diagnosing,
  title={Diagnosing and enhancing VAE models},
  author={Dai, Bin and Wipf, David},
  journal={arXiv preprint arXiv:1903.05789},
  year={2019}
}

% Datasets
% Emory

% CBIS-DDSM (segmentation labels) -- scanned film mammography
@article{lee2017curated,
  title={A curated mammography data set for use in computer-aided detection and diagnosis research},
  author={Lee, Rebecca Sawyer and Gimenez, Francisco and Hoogi, Assaf and Miyake, Kanae Kawai and Gorovoy, Mia and Rubin, Daniel L},
  journal={Scientific data},
  volume={4},
  number={1},
  pages={1--9},
  year={2017},
  publisher={Nature Publishing Group}
}


@article{kuznetsova2020open,
  title={The open images dataset v4: Unified image classification, object detection, and visual relationship detection at scale},
  author={Kuznetsova, Alina and Rom, Hassan and Alldrin, Neil and Uijlings, Jasper and Krasin, Ivan and Pont-Tuset, Jordi and Kamali, Shahab and Popov, Stefan and Malloci, Matteo and Kolesnikov, Alexander and others},
  journal={International Journal of Computer Vision},
  volume={128},
  number={7},
  pages={1956--1981},
  year={2020},
  publisher={Springer}
}

@article{feng2021curation,
  title={Curation of the candid-ptx dataset with free-text reports},
  author={Feng, Sijing and Azzollini, Damian and Kim, Ji Soo and Jin, Cheng-Kai and Gordon, Simon P and Yeoh, Jason and Kim, Eve and Han, Mina and Lee, Andrew and Patel, Aakash and others},
  journal={Radiology: Artificial Intelligence},
  volume={3},
  number={6},
  pages={e210136},
  year={2021},
  publisher={Radiological Society of North America}
}

@article{jeong2023emory,
  title={The EMory BrEast imaging Dataset (EMBED): A racially diverse, granular dataset of 3.4 million screening and diagnostic mammographic images},
  author={Jeong, Jiwoong J and Vey, Brianna L and Bhimireddy, Ananth and Kim, Thomas and Santos, Thiago and Correa, Ramon and Dutt, Raman and Mosunjac, Marina and Oprea-Ilies, Gabriela and Smith, Geoffrey and others},
  journal={Radiology: Artificial Intelligence},
  volume={5},
  number={1},
  pages={e220047},
  year={2023},
  publisher={Radiological Society of North America}
}

@article{nguyen2022vindrcxr,
  title={VinDr-CXR: An open dataset of chest X-rays with radiologist’s annotations},
  author={Nguyen, Ha Q and Lam, Khanh and Le, Linh T and Pham, Hieu H and Tran, Dat Q and Nguyen, Dung B and Le, Dung D and Pham, Chi M and Tong, Hang TT and Dinh, Diep H and others},
  journal={Scientific Data},
  volume={9},
  number={1},
  pages={429},
  year={2022},
  publisher={Nature Publishing Group UK London}
}

% 22347940 
@Article{europeanrad2011,
   Author={European Society of Radiology,"},
   Title="{{U}sability of irreversible image compression in radiological imaging. {A} position paper by the {E}uropean {S}ociety of {R}adiology ({E}{S}{R})}",
   Journal="Insights Imaging",
   Year="2011",
   Volume="2",
   Number="2",
   Pages="103--115",
   Month="Apr"
}

@article{wallace1992jpeg,
  title={The JPEG still picture compression standard},
  author={Wallace, Gregory K},
  journal={IEEE transactions on consumer electronics},
  volume={38},
  number={1},
  pages={xviii--xxxiv},
  year={1992},
  publisher={IEEE}
}

@inproceedings{dodge2016understanding,
  title={Understanding how image quality affects deep neural networks},
  author={Dodge, Samuel and Karam, Lina},
  booktitle={2016 eighth international conference on quality of multimedia experience (QoMEX)},
  pages={1--6},
  year={2016},
  organization={IEEE}
}


@article{kingma2013vae,
  title={Auto-encoding variational bayes},
  author={Kingma, Diederik P and Welling, Max},
  journal={arXiv preprint arXiv:1312.6114},
  year={2013}
}

@inproceedings{rezende2014stochastic,
  title={Stochastic backpropagation and approximate inference in deep generative models},
  author={Rezende, Danilo Jimenez and Mohamed, Shakir and Wierstra, Daan},
  booktitle={International conference on machine learning},
  pages={1278--1286},
  year={2014},
  organization={PMLR}
}

@article{shannon1948mathematical,
  title={A mathematical theory of communication},
  author={Shannon, Claude E},
  journal={The Bell system technical journal},
  volume={27},
  number={3},
  pages={379--423},
  year={1948},
  publisher={Nokia Bell Labs}
}

@inproceedings{szegedy2015inception,
  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{radford2021clip,
  title={Learning transferable visual models from natural language supervision},
  author={Radford, Alec and Kim, Jong Wook and Hallacy, Chris and Ramesh, Aditya and Goh, Gabriel and Agarwal, Sandhini and Sastry, Girish and Askell, Amanda and Mishkin, Pamela and Clark, Jack and others},
  booktitle={International conference on machine learning},
  pages={8748--8763},
  year={2021},
  organization={PMLR}
}

@article{zhang2023biomedclip,
  title={Large-scale domain-specific pretraining for biomedical vision-language processing},
  author={Zhang, Sheng and others},
  journal={arXiv preprint arXiv:2303.00915},
  year={2023}
}

@inproceedings{zhang2016probe,
  title={Colorful image colorization},
  author={Zhang, Richard and Isola, Phillip and Efros, Alexei A},
  booktitle={Computer Vision--ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part III 14},
  pages={649--666},
  year={2016},
  organization={Springer}
}

@article{kingma2014adam,
  title={Adam: A method for stochastic optimization},
  author={Kingma, Diederik P and Ba, Jimmy},
  journal={arXiv preprint arXiv:1412.6980},
  year={2014}
}


@inproceedings{dubois2021lossy,
 author = {Dubois, Yann and Bloem-Reddy, Benjamin and Ullrich, Karen and Maddison, Chris J},
 booktitle = {Advances in Neural Information Processing Systems},
 editor = {M. Ranzato and A. Beygelzimer and Y. Dauphin and P.S. Liang and J. Wortman Vaughan},
 pages = {14014--14028},
 publisher = {Curran Associates, Inc.},
 title = {Lossy Compression for Lossless Prediction},
 url = {https://proceedings.neurips.cc/paper_files/paper/2021/file/7535bbb91c8fde347ad861f293126633-Paper.pdf},
 volume = {34},
 year = {2021}
}

@Article{rsnamammo,
   Author={RSNA,"},
   Title="RSNA Screening Mammography Breast Cancer Detection",
   Journal="Kaggle.com",
   Year="2023",
}







@article{rsnaboneage,
author = {Halabi, Safwan  and others},
title = {The RSNA Pediatric Bone Age Machine Learning                     Challenge},
journal = {Radiology},
volume = {290},
number = {2},
pages = {498-503},
year = {2019},
doi = {10.1148/radiol.2018180736},
    note ={PMID: 30480490},

URL = { 
    
        https://doi.org/10.1148/radiol.2018180736
    
    

},
eprint = { 
    
        https://doi.org/10.1148/radiol.2018180736
    
    

}
}




@Article{Nagy2022wristfrac,
author={Nagy, Eszter
and Janisch, Michael
and Hr{\v{z}}i{\'{c}}, Franko
and Sorantin, Erich
and Tschauner, Sebastian},
title={A pediatric wrist trauma X-ray dataset (GRAZPEDWRI-DX) for machine learning},
journal={Scientific Data},
year={2022},
month={May},
day={20},
volume={9},
number={1},
pages={222},
abstract={Digital radiography is widely available and the standard modality in trauma imaging, often enabling to diagnose pediatric wrist fractures. However, image interpretation requires time-consuming specialized training. Due to astonishing progress in computer vision algorithms, automated fracture detection has become a topic of research interest. This paper presents the GRAZPEDWRI-DX dataset containing annotated pediatric trauma wrist radiographs of 6,091 patients, treated at the Department for Pediatric Surgery of the University Hospital Graz between 2008 and 2018. A total number of 10,643 studies (20,327 images) are made available, typically covering posteroanterior and lateral projections. The dataset is annotated with 74,459 image tags and features 67,771 labeled objects. We de-identified all radiographs and converted the DICOM pixel data to 16-Bit grayscale PNG images. The filenames and the accompanying text files provide basic patient information (age, sex). Several pediatric radiologists annotated dataset images by placing lines, bounding boxes, or polygons to mark pathologies like fractures or periosteal reactions. They also tagged general image characteristics. This dataset is publicly available to encourage computer vision research.},
issn={2052-4463},
doi={10.1038/s41597-022-01328-z},
url={https://doi.org/10.1038/s41597-022-01328-z}
}


@article{guan2020fracture,
title = {Arm fracture detection in X-rays based on improved deep convolutional neural network},
journal = {Computers & Electrical Engineering},
volume = {81},
pages = {106530},
year = {2020},
issn = {0045-7906},
doi = {https://doi.org/10.1016/j.compeleceng.2019.106530},
url = {https://www.sciencedirect.com/science/article/pii/S0045790618330878},
author = {Bin Guan and Guoshan Zhang and Jinkun Yao and Xinbo Wang and Mengxuan Wang},
}

@article{Naranje2016frac,
  doi = {10.1097/bpo.0000000000000595},
  url = {https://doi.org/10.1097/bpo.0000000000000595},
  year = {2016},
  month = jun,
  publisher = {Ovid Technologies (Wolters Kluwer Health)},
  volume = {36},
  number = {4},
  pages = {e45--e48},
  author = {Sameer M. Naranje and Richard A. Erali and William C. Warner and Jeffrey R. Sawyer and Derek M. Kelly},
  title = {Epidemiology of Pediatric Fractures Presenting to Emergency Departments in the United States},
  journal = {Journal of Pediatric Orthopaedics}
}

@article{DeSanctis2014boneage,
  doi = {10.4103/2230-8210.145076},
  url = {https://doi.org/10.4103/2230-8210.145076},
  year = {2014},
  publisher = {Medknow},
  volume = {18},
  number = {7},
  pages = {63},
  author = {Vincenzo De Sanctis and Salvatore Di Maio and AshrafT Soliman and Giuseppe Raiola and Rania Elalaily and Giuseppe Millimaggi},
  title = {Hand X-ray in pediatric endocrinology: Skeletal age assessment and beyond},
  journal = {Indian Journal of Endocrinology and Metabolism}
}

@incollection{bassettbreast,
title = {26 - Breast Imaging Screening and Diagnosis},
editor = {Kirby I. Bland and Edward M. Copeland and V. Suzanne Klimberg and William J. Gradishar},
booktitle = {The Breast (Fifth Edition)},
publisher = {Elsevier},
edition = {Fifth Edition},
pages = {337-361.e2},
year = {2018},
isbn = {978-0-323-35955-9},
doi = {https://doi.org/10.1016/B978-0-323-35955-9.00026-X},
url = {https://www.sciencedirect.com/science/article/pii/B978032335955900026X},
author = {Lawrence W. Bassett and Stephanie Lee-Felker},
keywords = {screening mammography, diagnostic mammography, breast ultrasound, breast magnetic resonance imaging, core needle biopsy, preoperative needle localization},
abstract = {This chapter reviews the most commonly used imaging modalities in the screening and diagnosis of breast diseases. Topics discussed include characteristic benign and malignant imaging findings on mammography, breast ultrasound, and breast magnetic resonance imaging, as well as common image-guided breast interventional procedures, including core needle biopsy and preoperative needle localization.}
}

@inproceedings{pytorch,
 author = {Paszke, Adam and others},
 booktitle = {Advances in Neural Information Processing Systems},
 editor = {H. Wallach and H. Larochelle and A. Beygelzimer and F. d\textquotesingle Alch\'{e}-Buc and E. Fox and R. Garnett},
 pages = {},
 publisher = {Curran Associates, Inc.},
 title = {PyTorch: An Imperative Style, High-Performance Deep Learning Library},
 url = {https://proceedings.neurips.cc/paper_files/paper/2019/file/bdbca288fee7f92f2bfa9f7012727740-Paper.pdf},
 volume = {32},
 year = {2019}
}

@misc{wantlin2023benchmd,
      title={BenchMD: A Benchmark for Modality-Agnostic Learning on Medical Images and Sensors}, 
      author={Kathryn Wantlin and others},
      year={2023},
      eprint={2304.08486},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

@article{Varma2019,
  doi = {10.1038/s42256-019-0126-0},
  url = {https://doi.org/10.1038/s42256-019-0126-0},
  year = {2019},
  month = dec,
  publisher = {Springer Science and Business Media {LLC}},
  volume = {1},
  number = {12},
  pages = {578--583},
  author = {Maya Varma and others},
  title = {Automated abnormality detection in lower extremity radiographs using deep learning},
  journal = {Nature Machine Intelligence}
}


@InProceedings{convirt,
  title = 	 {Contrastive Learning of Medical Visual Representations from Paired Images and Text},
  author =       {Zhang, Yuhao and Jiang, Hang and Miura, Yasuhide and Manning, Christopher D. and Langlotz, Curtis P.},
  booktitle = 	 {Proceedings of the 7th Machine Learning for Healthcare Conference},
  pages = 	 {2--25},
  year = 	 {2022},
  editor = 	 {Lipton, Zachary and Ranganath, Rajesh and Sendak, Mark and Sjoding, Michael and Yeung, Serena},
  volume = 	 {182},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {05--06 Aug},
  publisher =    {PMLR},
  pdf = 	 {https://proceedings.mlr.press/v182/zhang22a/zhang22a.pdf},
  url = 	 {https://proceedings.mlr.press/v182/zhang22a.html},
}

@InProceedings{Huang_2021_ICCV,
    author    = {Huang, Shih-Cheng and Shen, Liyue and Lungren, Matthew P. and Yeung, Serena},
    title     = {GLoRIA: A Multimodal Global-Local Representation Learning Framework for Label-Efficient Medical Image Recognition},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {3942-3951}
}

@misc{miura2021improving,
      title={Improving Factual Completeness and Consistency of Image-to-Text Radiology Report Generation}, 
      author={Yasuhide Miura and Yuhao Zhang and Emily Bao Tsai and Curtis P. Langlotz and Dan Jurafsky},
      year={2021},
      eprint={2010.10042},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

@article{Tiu2022,
  doi = {10.1038/s41551-022-00936-9},
  url = {https://doi.org/10.1038/s41551-022-00936-9},
  year = {2022},
  month = sep,
  publisher = {Springer Science and Business Media {LLC}},
  volume = {6},
  number = {12},
  pages = {1399--1406},
  author = {Ekin Tiu and Ellie Talius and Pujan Patel and Curtis P. Langlotz and Andrew Y. Ng and Pranav Rajpurkar},
  title = {Expert-level detection of pathologies from unannotated chest X-ray images via self-supervised learning},
  journal = {Nature Biomedical Engineering}
}

@ARTICLE{Liu4K,
      author={J. {Liu} and D. {Liu} and W. {Yang} and S. {Xia} and X. {Zhang} and Y. {Dai}},
      journal={IEEE Transactions on Image Processing}, 
      title={A Comprehensive Benchmark for Single Image Compression Artifact Reduction}, 
      year={2020},
      volume={29},
      number={},
      pages={7845-7860},
}

@misc{msssim,
      title={Learning to Generate Images with Perceptual Similarity Metrics}, 
      author={Jake Snell and Karl Ridgeway and Renjie Liao and Brett D. Roads and Michael C. Mozer and Richard S. Zemel},
      year={2017},
      eprint={1511.06409},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

@misc{patel2019human,
      title={Human Perceptual Evaluations for Image Compression}, 
      author={Yash Patel and Srikar Appalaraju and R. Manmatha},
      year={2019},
      eprint={1908.04187},
      archivePrefix={arXiv},
      primaryClass={eess.IV}
}

@misc{zhang2018unreasonable,
      title={The Unreasonable Effectiveness of Deep Features as a Perceptual Metric}, 
      author={Richard Zhang and Phillip Isola and Alexei A. Efros and Eli Shechtman and Oliver Wang},
      year={2018},
      archivePrefix={CVPR},
}

@InProceedings{psnr,
author="Kotevski, Zoran
and Mitrevski, Pece",
editor="Davcev, Danco
and G{\'o}mez, Jorge Marx",
title="Experimental Comparison of PSNR and SSIM Metrics for Video Quality Estimation",
booktitle="ICT Innovations 2009",
year="2010",
publisher="Springer Berlin Heidelberg",
address="Berlin, Heidelberg",
pages="357--366",
abstract="Since the development of digital video technology, due to the nature of digital video, the approach to video quality estimation has changed. Basically there are two types of metrics used to measure the objective quality of processed digital video: purely mathematically defined video quality metrics (DELTA, MSAD, MSE, SNR and PSNR) where the error is mathematically calculated as a difference between the original and processed pixel, and video quality metrics that have similar characteristics as the Human Visual System -- HVS (SSIM, NQI, VQM) where the perceptual quality is considered in the overall video quality estimation. In this paper, an overview and experimental comparison of PSNR and SSIM metrics for video quality estimation is presented.",
isbn="978-3-642-10781-8"
}

@misc{park2023rgb,
      title={RGB no more: Minimally-decoded JPEG Vision Transformers}, 
      author={Jeongsoo Park and Justin Johnson},
      year={2023},
      archivePrefix={CVPR},
}

@misc{wiles2022compressed,
      title={Compressed Vision for Efficient Video Understanding}, 
      author={Olivia Wiles and Joao Carreira and Iain Barr and Andrew Zisserman and Mateusz Malinowski},
      year={2022},
      eprint={2210.02995},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

@misc{ronneberger2015unet,
      title={U-Net: Convolutional Networks for Biomedical Image Segmentation}, 
      author={Olaf Ronneberger and Philipp Fischer and Thomas Brox},
      year={2015},
      archivePrefix={MICCAI},
}

@misc{bakas2019identifying,
      title={Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge}, 
      author={Spyridon Bakas and Mauricio Reyes and Andras Jakab and Stefan Bauer and others},
      year={2019},
      eprint={1811.02629},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}
@Article{Bakas2017,
author={Bakas, Spyridon
and Akbari, Hamed
and Sotiras, Aristeidis
and Bilello, Michel
and Rozycki, Martin
and Kirby, Justin S.
and Freymann, John B.
and Farahani, Keyvan
and Davatzikos, Christos},
title={Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features},
journal={Scientific Data},
year={2017},
month={Sep},
day={05},
volume={4},
number={1},
pages={170117},
issn={2052-4463},
doi={10.1038/sdata.2017.117},
url={https://doi.org/10.1038/sdata.2017.117}
}


@ARTICLE{menze2015,
  author={Menze, Bjoern H. and Jakab, Andras and Bauer, Stefan and Kalpathy-Cramer, Jayashree and others},
  journal={IEEE Transactions on Medical Imaging}, 
  title={The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)}, 
  year={2015},
  volume={34},
  number={10},
  pages={1993-2024},
  doi={10.1109/TMI.2014.2377694}}


@article{begaint2020compressai,
	title={CompressAI: a PyTorch library and evaluation platform for end-to-end compression research},
	author={B{\'e}gaint, Jean and Racap{\'e}, Fabien and Feltman, Simon and Pushparaja, Akshay},
	year={2020},
	journal={arXiv preprint arXiv:2011.03029},
}

@misc{ballé2018hyperprior,
      title={Variational image compression with a scale hyperprior}, 
      author={Johannes Ballé and David Minnen and Saurabh Singh and Sung Jin Hwang and Nick Johnston},
      year={2018},
      archivePrefix={International Conference on Learning Representations},
}

@misc{cheng2020learned,
      title={Learned Image Compression with Discretized Gaussian Mixture Likelihoods and Attention Modules}, 
      author={Zhengxue Cheng and Heming Sun and Masaru Takeuchi and Jiro Katto},
      year={2020},
      archivePrefix={CVPR 2020},
      primaryClass={eess.IV}
}

@misc{chen2020simclr,
      title={A Simple Framework for Contrastive Learning of Visual Representations}, 
      author={Ting Chen and Simon Kornblith and Mohammad Norouzi and Geoffrey Hinton},
      year={2020},
      eprint={2002.05709},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

@article{bilinear,
title = {Bilinear interpolation of digital images},
journal = {Ultramicroscopy},
volume = {6},
number = {2},
pages = {201-204},
year = {1981},
issn = {0304-3991},
doi = {https://doi.org/10.1016/0304-3991(81)90061-9},
url = {https://www.sciencedirect.com/science/article/pii/0304399181900619},
author = {P.R. Smith},
abstract = {The application of the method of three-point bilinear interpolation is shown to generate a smoothly interpolated image, free from erroneous substructure generated by the interpolation scheme itself. Three-point interpolation is therefore to be preferred to the standard four-point bilinear scheme when images are prepared for film writing.}
}

@article{superressurvey,
title = {Image super-resolution survey},
journal = {Image and Vision Computing},
volume = {24},
number = {10},
pages = {1039-1052},
year = {2006},
issn = {0262-8856},
doi = {https://doi.org/10.1016/j.imavis.2006.02.026},
url = {https://www.sciencedirect.com/science/article/pii/S0262885606001089},
author = {J.D. {van Ouwerkerk}},
keywords = {Super-resolution, Single frame},
abstract = {The shortcomings in commonly used kernel-based super-resolution drive the study of improved super-resolution algorithms of higher quality. In the past years a wide range of very different approaches has been taken to improve super-resolution. This paper compares approaches to high quality super-resolution by looking at theoretical backgrounds and practical results. Strengths and weaknesses are listed with the intent to spot chances for combination or improvement of techniques, thereby forming a base for future improved super-resolution algorithms.}
}

@inproceedings{
adamson2023using,
title={Using Deep Feature Distances for Evaluating {MR} Image Reconstruction Quality},
author={Philip M Adamson and Arjun D Desai and Jeffrey Dominic and Christian Bluethgen and Jeff P. Wood and Ali B Syed and Robert D. Boutin and Kathryn J. Stevens and Shreyas Vasanawala and John M. Pauly and Akshay S Chaudhari and Beliz Gunel},
booktitle={NeurIPS 2023 Workshop on Deep Learning and Inverse Problems},
year={2023},
url={https://openreview.net/forum?id=AUiZyqYiGb}
}







@article{sabottke2020resolution,
author = {Sabottke, Carl F. and Spieler, Bradley M.},
title = {The Effect of Image Resolution on Deep Learning in                     Radiography},
journal = {Radiology: Artificial Intelligence},
volume = {2},
number = {1},
pages = {e190015},
year = {2020},
doi = {10.1148/ryai.2019190015},
    note ={PMID: 33937810},

URL = { 
    
        https://doi.org/10.1148/ryai.2019190015
    
    

},
eprint = { 
    
        https://doi.org/10.1148/ryai.2019190015
    
    

}
,
    abstract = { Purpose To examine variations of convolutional neural network (CNN) performance for multiple chest radiograph diagnoses and image resolutions. Materials and Methods This retrospective study examined CNN performance using the publicly available National Institutes of Health chest radiograph dataset comprising 112 120 chest radiographic images from 30 805 patients. The network architectures examined included ResNet34 and DenseNet121. Image resolutions ranging from 32 × 32 to 600 × 600 pixels were investigated. Network training paradigms used 80\% of samples for training and 20\% for validation. CNN performance was evaluated based on area under the receiver operating characteristic curve (AUC) and label accuracy. Binary output networks were trained separately for each label or diagnosis under consideration. Results Maximum AUCs were achieved at image resolutions between 256 × 256 and 448 × 448 pixels for binary decision networks targeting emphysema, cardiomegaly, hernias, edema, effusions, atelectasis, masses, and nodules. When comparing performance between networks that utilize lower resolution (64 × 64 pixels) versus higher (320 × 320 pixels) resolution inputs, emphysema, cardiomegaly, hernia, and pulmonary nodule detection had the highest fractional improvements in AUC at higher image resolutions. Specifically, pulmonary nodule detection had an AUC performance ratio of 80.7\% ± 1.5 (standard deviation) (0.689 of 0.854) whereas thoracic mass detection had an AUC ratio of 86.7\% ± 1.2 (0.767 of 0.886) for these image resolutions. Conclusion Increasing image resolution for CNN training often has a trade-off with the maximum possible batch size, yet optimal selection of image resolution has the potential for further increasing neural network performance for various radiology-based machine learning tasks. Furthermore, identifying diagnosis-specific tasks that require relatively higher image resolution can potentially provide insight into the relative difficulty of identifying different radiology findings. Supplemental material is available for this article. Keywords: Computer Aided Diagnosis (CAD), Conventional Radiography, Convolutional Neural Network (CNN), Neural Networks, Thorax © RSNA, 2020 See also the commentary by Lakhani in this issue. }
}



@misc{zhou2023mae,
      title={Self Pre-training with Masked Autoencoders for Medical Image Classification and Segmentation}, 
      author={Lei Zhou and Huidong Liu and Joseph Bae and Junjun He and Dimitris Samaras and Prateek Prasanna},
      year={2023},
      eprint={2203.05573},
      archivePrefix={arXiv},
      primaryClass={eess.IV}
}

@article{loffler2020vertebral,
  title={A vertebral segmentation dataset with fracture grading},
  author={L{\"o}ffler, Maximilian T and Sekuboyina, Anjany and Jacob, Alina and Grau, Anna-Lena and Scharr, Andreas and El Husseini, Malek and Kallweit, Mareike and Zimmer, Claus and Baum, Thomas and Kirschke, Jan S},
  journal={Radiology: Artificial Intelligence},
  volume={2},
  number={4},
  pages={e190138},
  year={2020},
  publisher={Radiological Society of North America}
}


@misc{he2021masked,
      title={Masked Autoencoders Are Scalable Vision Learners}, 
      author={Kaiming He and Xinlei Chen and Saining Xie and Yanghao Li and Piotr Dollár and Ross Girshick},
      year={2021},
      eprint={2111.06377},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

@misc{hu2021lora,
      title={LoRA: Low-Rank Adaptation of Large Language Models}, 
      author={Edward J. Hu and others},
      year={2021},
      eprint={2106.09685},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

@misc{lee2023llmcxr,
      title={LLM-CXR: Instruction-Finetuned LLM for CXR Image Understanding and Generation}, 
      author={Suhyeon Lee and Won Jun Kim and Jinho Chang and Jong Chul Ye},
      year={2023},
      eprint={2305.11490},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}


@INPROCEEDINGS{lpips,
  author={Zhang, Richard and Isola, Phillip and Efros, Alexei A. and Shechtman, Eli and Wang, Oliver},
  booktitle={2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition}, 
  title={The Unreasonable Effectiveness of Deep Features as a Perceptual Metric}, 
  year={2018},
  volume={},
  number={},
  pages={586-595},
  doi={10.1109/CVPR.2018.00068}}

@misc{isola2018patchgan,
      title={Image-to-Image Translation with Conditional Adversarial Networks}, 
      author={Phillip Isola and Jun-Yan Zhu and Tinghui Zhou and Alexei A. Efros},
      year={2018},
      eprint={1611.07004},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

@misc{mesterhazy2020high,
      title={High performance on-demand de-identification of a petabyte-scale medical imaging data lake}, 
      author={Joseph Mesterhazy and Garrick Olson and Somalee Datta},
      year={2020},
      eprint={2008.01827},
      archivePrefix={arXiv},
      primaryClass={cs.DC}
}

@article{engin2020,
author = {Engin Dikici and Matthew Bigelow and Luciano M. Prevedello and Richard D. White and Barbaros S. Erdal},
title = {{Integrating AI into radiology workflow: levels of research, production, and feedback maturity}},
volume = {7},
journal = {Journal of Medical Imaging},
number = {1},
publisher = {SPIE},
pages = {016502},
keywords = {digital imaging and communications in medicine, picture archiving and communication system, AI-based image analysis, radiology workflow, Artificial intelligence, Data modeling, Picture Archiving and Communication System, Radiology, Medical imaging, Process modeling, Image processing, Magnetic resonance imaging, Evolutionary algorithms, 3D modeling},
year = {2020},
doi = {10.1117/1.JMI.7.1.016502},
URL = {https://doi.org/10.1117/1.JMI.7.1.016502}
}


@Article{najjar2023,
AUTHOR = {Najjar, Reabal},
TITLE = {Redefining Radiology: A Review of Artificial Intelligence Integration in Medical Imaging},
JOURNAL = {Diagnostics},
VOLUME = {13},
YEAR = {2023},
NUMBER = {17},
ARTICLE-NUMBER = {2760},
URL = {https://www.mdpi.com/2075-4418/13/17/2760},
PubMedID = {37685300},
ISSN = {2075-4418},
ABSTRACT = {This comprehensive review unfolds a detailed narrative of Artificial Intelligence (AI) making its foray into radiology, a move that is catalysing transformational shifts in the healthcare landscape. It traces the evolution of radiology, from the initial discovery of X-rays to the application of machine learning and deep learning in modern medical image analysis. The primary focus of this review is to shed light on AI applications in radiology, elucidating their seminal roles in image segmentation, computer-aided diagnosis, predictive analytics, and workflow optimisation. A spotlight is cast on the profound impact of AI on diagnostic processes, personalised medicine, and clinical workflows, with empirical evidence derived from a series of case studies across multiple medical disciplines. However, the integration of AI in radiology is not devoid of challenges. The review ventures into the labyrinth of obstacles that are inherent to AI-driven radiology—data quality, the ’black box’ enigma, infrastructural and technical complexities, as well as ethical implications. Peering into the future, the review contends that the road ahead for AI in radiology is paved with promising opportunities. It advocates for continuous research, embracing avant-garde imaging technologies, and fostering robust collaborations between radiologists and AI developers. The conclusion underlines the role of AI as a catalyst for change in radiology, a stance that is firmly rooted in sustained innovation, dynamic partnerships, and a steadfast commitment to ethical responsibility.},
DOI = {10.3390/diagnostics13172760}
}



@article{jack2008alzheimer,
  title={The Alzheimer's disease neuroimaging initiative (ADNI): MRI methods},
  author={Jack Jr, Clifford R and others},
  journal={Journal of Magnetic Resonance Imaging: An Official Journal of the International Society for Magnetic Resonance in Medicine},
  volume={27},
  number={4},
  pages={685--691},
  year={2008},
  publisher={Wiley Online Library}
}

@article{dagley2017harvard,
  title={Harvard aging brain study: dataset and accessibility},
  author={Dagley, Alexander and LaPoint, Molly and Huijbers, Willem and Hedden, Trey and McLaren, Donald G and Chatwal, Jasmeer P and Papp, Kathryn V and Amariglio, Rebecca E and Blacker, Deborah and Rentz, Dorene M and others},
  journal={Neuroimage},
  volume={144},
  pages={255--258},
  year={2017},
  publisher={Elsevier}
}

@article{insel2020a4,
  title={The A4 study: $\beta$-amyloid and cognition in 4432 cognitively unimpaired adults},
  author={Insel, Philip S and Donohue, Michael C and Sperling, Reisa and Hansson, Oskar and Mattsson-Carlgren, Niklas},
  journal={Annals of Clinical and Translational Neurology},
  volume={7},
  number={5},
  pages={776--785},
  year={2020},
  publisher={Wiley Online Library}
}

@article{lamontagne2019oasis,
  title={OASIS-3: longitudinal neuroimaging, clinical, and cognitive dataset for normal aging and Alzheimer disease},
  author={LaMontagne, Pamela J and  others},
  journal={MedRxiv},
  pages={2019--12},
  year={2019},
  publisher={Cold Spring Harbor Laboratory Press}
}


@article{bien2018deep,
  title={Deep-learning-assisted diagnosis for knee magnetic resonance imaging: development and retrospective validation of MRNet},
  author={Bien, Nicholas and others},
  journal={PLoS medicine},
  volume={15},
  number={11},
  pages={e1002699},
  year={2018},
  publisher={Public Library of Science San Francisco, CA USA}
}

@article{armato2011lung,
  title={The lung image database consortium (LIDC) and image database resource initiative (IDRI): a completed reference database of lung nodules on CT scans},
  author={Armato III, Samuel G and others},
  journal={Medical physics},
  volume={38},
  number={2},
  pages={915--931},
  year={2011},
  publisher={Wiley Online Library}
}

@article{hooper2021impact,
  title={Impact of upstream medical image processing on downstream performance of a head CT triage neural network},
  author={Hooper, Sarah M and others},
  journal={Radiology: Artificial Intelligence},
  volume={3},
  number={4},
  pages={e200229},
  year={2021},
  publisher={Radiological Society of North America}
}

@article{chilamkurthy2018development,
  title={Development and validation of deep learning algorithms for detection of critical findings in head CT scans},
  author={Chilamkurthy, Sasank and Ghosh, Rohit and Tanamala, Swetha and Biviji, Mustafa and Campeau, Norbert G and Venugopal, Vasantha Kumar and Mahajan, Vidur and Rao, Pooja and Warier, Prashant},
  journal={arXiv preprint arXiv:1803.05854},
  year={2018}
}

@article{wasserthal2023totalsegmentator,
  title={Totalsegmentator: Robust segmentation of 104 anatomic structures in ct images},
  author={Wasserthal, Jakob and others},
  journal={Radiology: Artificial Intelligence},
  volume={5},
  number={5},
  year={2023},
  publisher={Radiological Society of North America}
}

@article{ji2022amos,
  title={Amos: A large-scale abdominal multi-organ benchmark for versatile medical image segmentation},
  author={Ji, Yuanfeng and others},
  journal={Advances in Neural Information Processing Systems},
  volume={35},
  pages={36722--36732},
  year={2022}
}

@misc{stanfordaimi_coca_2024,
  title = {{COCA - Coronary Calcium and Chest CTs}},
  author = {{Stanford Center for Artificial Intelligence in Medicine \& Imaging (AIMI)}},
  year = {2024},
  howpublished = {\url{https://stanfordaimi.azurewebsites.net/datasets/e8ca74dc-8dd4-4340-815a-60b41f6cb2aa}},
  note = {Accessed: 2024-03-06}
}

@inproceedings{zhang2022adapting,
  title={Adapting Pre-trained Vision Transformers from 2D to 3D through Weight Inflation Improves Medical Image Segmentation},
  author={Zhang, Yuhui and Huang, Shih-Cheng and Zhou, Zhengping and Lungren, Matthew P and Yeung, Serena},
  booktitle={Machine Learning for Health},
  pages={391--404},
  year={2022},
  organization={PMLR}
}

@inproceedings{hu2018squeeze,
  title={Squeeze-and-excitation networks},
  author={Hu, Jie and Shen, Li and Sun, Gang},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={7132--7141},
  year={2018}
}

@inproceedings{vincent2008,
author = {Vincent, Pascal and Larochelle, Hugo and Bengio, Yoshua and Manzagol, Pierre-Antoine},
title = {Extracting and composing robust features with denoising autoencoders},
year = {2008},
isbn = {9781605582054},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390156.1390294},
doi = {10.1145/1390156.1390294},
abstract = {Previous work has shown that the difficulties in learning deep generative or discriminative models can be overcome by an initial unsupervised learning step that maps inputs to useful intermediate representations. We introduce and motivate a new training principle for unsupervised learning of a representation based on the idea of making the learned representations robust to partial corruption of the input pattern. This approach can be used to train autoencoders, and these denoising autoencoders can be stacked to initialize deep architectures. The algorithm can be motivated from a manifold learning and information theoretic perspective or from a generative model perspective. Comparative experiments clearly show the surprising advantage of corrupting the input of autoencoders on a pattern classification benchmark suite.},
booktitle = {Proceedings of the 25th International Conference on Machine Learning},
pages = {1096–1103},
numpages = {8},
location = {Helsinki, Finland},
series = {ICML '08}
}

@InProceedings{pinaya2022,
author="Pinaya, Walter H. L.
and Tudosiu, Petru-Daniel
and Dafflon, Jessica
and Da Costa, Pedro F.
and Fernandez, Virginia
and Nachev, Parashkev
and Ourselin, Sebastien
and Cardoso, M. Jorge",
editor="Mukhopadhyay, Anirban
and Oksuz, Ilkay
and Engelhardt, Sandy
and Zhu, Dajiang
and Yuan, Yixuan",
title="Brain Imaging Generation with Latent Diffusion Models",
booktitle="Deep Generative Models",
year="2022",
publisher="Springer Nature Switzerland",
address="Cham",
pages="117--126",
abstract="Deep neural networks have brought remarkable breakthroughs in medical image analysis. However, due to their data-hungry nature, the modest dataset sizes in medical imaging projects might be hindering their full potential. Generating synthetic data provides a promising alternative, allowing to complement training datasets and conducting medical image research at a larger scale. Diffusion models recently have caught the attention of the computer vision community by producing photorealistic synthetic images. In this study, we explore using Latent Diffusion Models to generate synthetic images from high-resolution 3D brain images. We used T1w MRI images from the UK Biobank dataset (N = 31,740) to train our models to learn about the probabilistic distribution of brain images, conditioned on covariates, such as age, sex, and brain structure volumes. We found that our models created realistic data, and we could use the conditioning variables to control the data generation effectively. Besides that, we created a synthetic dataset with 100,000 brain images and made it openly available to the scientific community.",
isbn="978-3-031-18576-2"
}


@ARTICLE{guan2022,
  author={Guan, Hao and Liu, Mingxia},
  journal={IEEE Transactions on Biomedical Engineering}, 
  title={Domain Adaptation for Medical Image Analysis: A Survey}, 
  year={2022},
  volume={69},
  number={3},
  pages={1173-1185},
  keywords={Biomedical imaging;Image analysis;Task analysis;Adaptation models;Transfer learning;Magnetic resonance imaging;Image segmentation;Domain adaptation;domain shift;machine learning;deep learning;medical image analysis},
  doi={10.1109/TBME.2021.3117407}}

@InProceedings{Esser_2021_CVPR,
    author    = {Esser, Patrick and Rombach, Robin and Ommer, Bjorn},
    title     = {Taming Transformers for High-Resolution Image Synthesis},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2021},
    pages     = {12873-12883}
}

@article{Bien2018,
  title = {Deep-learning-assisted diagnosis for knee magnetic resonance imaging: Development and retrospective validation of MRNet},
  volume = {15},
  ISSN = {1549-1676},
  url = {http://dx.doi.org/10.1371/journal.pmed.1002699},
  DOI = {10.1371/journal.pmed.1002699},
  number = {11},
  journal = {PLOS Medicine},
  publisher = {Public Library of Science (PLoS)},
  author = {Bien,  Nicholas and Rajpurkar,  Pranav and Ball,  Robyn L. and Irvin,  Jeremy and Park,  Allison and Jones,  Erik and Bereket,  Michael and Patel,  Bhavik N. and Yeom,  Kristen W. and Shpanskaya,  Katie and Halabi,  Safwan and Zucker,  Evan and Fanton,  Gary and Amanatullah,  Derek F. and Beaulieu,  Christopher F. and Riley,  Geoffrey M. and Stewart,  Russell J. and Blankenberg,  Francis G. and Larson,  David B. and Jones,  Ricky H. and Langlotz,  Curtis P. and Ng,  Andrew Y. and Lungren,  Matthew P.},
  editor = {Saria,  Suchi},
  year = {2018},
  month = nov,
  pages = {e1002699}
}



@misc{chambon2022adapting,
      title={Adapting Pretrained Vision-Language Foundational Models to Medical Imaging Domains}, 
      author={Pierre Chambon and Christian Bluethgen and Curtis P. Langlotz and Akshay Chaudhari},
      year={2022},
      eprint={2210.04133},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

@misc{bommasani2022opportunities,
      title={On the Opportunities and Risks of Foundation Models}, 
      author={Rishi Bommasani and others},
      year={2022},
      eprint={2108.07258},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

@article{yang2022introduction,
  title={An introduction to neural data compression},
  author={Yang, Yibo and Mandt, Stephan and Theis, Lucas},
  journal={arXiv preprint arXiv:2202.06533},
  year={2022}
}

@article{dupont2022coin++,
  title={Coin++: Data agnostic neural compression},
  author={Dupont, Emilien and Loya, Hrushikesh and Alizadeh, Milad and Goli{\'n}ski, Adam and Teh, Yee Whye and Doucet, Arnaud},
  journal={arXiv preprint arXiv:2201.12904},
  year={2022}
}

@inproceedings{sushmit2019x,
  title={X-ray image compression using convolutional recurrent neural networks},
  author={Sushmit, Asif Shahriyar and Zaman, Shakib Uz and Humayun, Ahmed Imtiaz and Hasan, Taufiq and Bhuiyan, Mohammed Imamul Hassan},
  booktitle={2019 IEEE EMBS International Conference on Biomedical \& Health Informatics (BHI)},
  pages={1--4},
  year={2019},
  organization={IEEE}
}

@article{panagiotidis1996neural,
  title={Neural network-assisted effective lossy compression of medical images},
  author={Panagiotidis, Nikos G and Kalogeras, Dimitris and Kollias, Stefanos D and Stafylopatis, Andreas},
  journal={Proceedings of the IEEE},
  volume={84},
  number={10},
  pages={1474--1487},
  year={1996},
  publisher={IEEE}
}

@article{goodfellow2020generative,
  title={Generative adversarial networks},
  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={Communications of the ACM},
  volume={63},
  number={11},
  pages={139--144},
  year={2020},
  publisher={ACM New York, NY, USA}
}

@article{balle2016end,
  title={End-to-end optimized image compression},
  author={Ball{\'e}, Johannes and Laparra, Valero and Simoncelli, Eero P},
  journal={arXiv preprint arXiv:1611.01704},
  year={2016}
}

@article{theis2017lossy,
  title={Lossy image compression with compressive autoencoders},
  author={Theis, Lucas and Shi, Wenzhe and Cunningham, Andrew and Husz{\'a}r, Ferenc},
  journal={arXiv preprint arXiv:1703.00395},
  year={2017}
}

@inproceedings{zhou2018variational,
  title={Variational autoencoder for low bit-rate image compression},
  author={Zhou, Lei and Cai, Chunlei and Gao, Yue and Su, Sanbao and Wu, Junmin},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops},
  pages={2617--2620},
  year={2018}
}

@misc{compressioncc,
    title = {CLIC 2022, the 5th Workshop and Challenge on Learned Image Compression},
    howpublished = {\url{https://compression.cc/}}
}

@article{tellez2019neural,
  title={Neural image compression for gigapixel histopathology image analysis},
  author={Tellez, David and Litjens, Geert and van der Laak, Jeroen and Ciompi, Francesco},
  journal={IEEE transactions on pattern analysis and machine intelligence},
  volume={43},
  number={2},
  pages={567--578},
  year={2019},
  publisher={IEEE}
}

@inproceedings{aswolinskiy2021neural,
  title={Neural image compression for non-small cell lung cancer subtype classification in H\&E stained whole-slide images},
  author={Aswolinskiy, Witali and Tellez, David and Raya, Gabriel and van der Woude, Lieke and Looijen-Salamon, Monika and van der Laak, Jeroen and Grunberg, Katrien and Ciompi, Francesco},
  booktitle={Medical Imaging 2021: Digital Pathology},
  volume={11603},
  pages={1160304},
  year={2021},
  organization={SPIE}
}

@inproceedings{tellez2020extending,
  title={Extending unsupervised neural image compression with supervised multitask learning},
  author={Tellez, David and H{\"o}ppener, Diederik and Verhoef, Cornelis and Gr{\"u}nhagen, Dirk and Nierop, Pieter and Drozdzal, Michal and Laak, Jeroen and Ciompi, Francesco},
  booktitle={Medical Imaging with Deep Learning},
  pages={770--783},
  year={2020},
  organization={PMLR}
}

@article{dai2019diagnosing,
  title={Diagnosing and enhancing VAE models},
  author={Dai, Bin and Wipf, David},
  journal={arXiv preprint arXiv:1903.05789},
  year={2019}
}

% Datasets
% Emory

% CBIS-DDSM (segmentation labels) -- scanned film mammography
@article{lee2017curated,
  title={A curated mammography data set for use in computer-aided detection and diagnosis research},
  author={Lee, Rebecca Sawyer and Gimenez, Francisco and Hoogi, Assaf and Miyake, Kanae Kawai and Gorovoy, Mia and Rubin, Daniel L},
  journal={Scientific data},
  volume={4},
  number={1},
  pages={1--9},
  year={2017},
  publisher={Nature Publishing Group}
}


@article{kuznetsova2020open,
  title={The open images dataset v4: Unified image classification, object detection, and visual relationship detection at scale},
  author={Kuznetsova, Alina and Rom, Hassan and Alldrin, Neil and Uijlings, Jasper and Krasin, Ivan and Pont-Tuset, Jordi and Kamali, Shahab and Popov, Stefan and Malloci, Matteo and Kolesnikov, Alexander and others},
  journal={International Journal of Computer Vision},
  volume={128},
  number={7},
  pages={1956--1981},
  year={2020},
  publisher={Springer}
}

@article{feng2021curation,
  title={Curation of the candid-ptx dataset with free-text reports},
  author={Feng, Sijing and Azzollini, Damian and Kim, Ji Soo and Jin, Cheng-Kai and Gordon, Simon P and Yeoh, Jason and Kim, Eve and Han, Mina and Lee, Andrew and Patel, Aakash and others},
  journal={Radiology: Artificial Intelligence},
  volume={3},
  number={6},
  pages={e210136},
  year={2021},
  publisher={Radiological Society of North America}
}


@article{jeong2023emory,
  title={The EMory BrEast imaging Dataset (EMBED): A racially diverse, granular dataset of 3.4 million screening and diagnostic mammographic images},
  author={Jeong, Jiwoong J and Vey, Brianna L and Bhimireddy, Ananth and Kim, Thomas and Santos, Thiago and Correa, Ramon and Dutt, Raman and Mosunjac, Marina and Oprea-Ilies, Gabriela and Smith, Geoffrey and others},
  journal={Radiology: Artificial Intelligence},
  volume={5},
  number={1},
  pages={e220047},
  year={2023},
  publisher={Radiological Society of North America}
}

@article{nguyen2022vindrcxr,
  title={VinDr-CXR: An open dataset of chest X-rays with radiologist’s annotations},
  author={Nguyen, Ha Q and Lam, Khanh and Le, Linh T and Pham, Hieu H and Tran, Dat Q and Nguyen, Dung B and Le, Dung D and Pham, Chi M and Tong, Hang TT and Dinh, Diep H and others},
  journal={Scientific Data},
  volume={9},
  number={1},
  pages={429},
  year={2022},
  publisher={Nature Publishing Group UK London}
}


@article{chambon2022roentgen,
  title={{RoentGen: Vision-Language Foundation Model for Chest X-ray Generation}},
  author={Chambon, Pierre and Bluethgen, Christian and Delbrouck, Jean-Benoit and Van der Sluijs, Rogier and Po{\l}acin, Ma{\l}gorzata and Chaves, Juan Manuel Zambrano and Abraham, Tanishq Mathew and Purohit, Shivanshu and Langlotz, Curtis P and Chaudhari, Akshay},
  journal={arXiv preprint arXiv:2211.12737},
  year={2022}
}

% 22347940 
@Article{europeanrad2011,
   Author="European Society of Radiology",
   Title="{{U}sability of irreversible image compression in radiological imaging. {A} position paper by the {E}uropean {S}ociety of {R}adiology ({E}{S}{R})}",
   Journal="Insights Imaging",
   Year="2011",
   Volume="2",
   Number="2",
   Pages="103--115",
   Month="Apr"
}


@article{wallace1992jpeg,
  title={The JPEG still picture compression standard},
  author={Wallace, Gregory K},
  journal={IEEE transactions on consumer electronics},
  volume={38},
  number={1},
  pages={xviii--xxxiv},
  year={1992},
  publisher={IEEE}
}

@inproceedings{dodge2016understanding,
  title={Understanding how image quality affects deep neural networks},
  author={Dodge, Samuel and Karam, Lina},
  booktitle={2016 eighth international conference on quality of multimedia experience (QoMEX)},
  pages={1--6},
  year={2016},
  organization={IEEE}
}

@article{yang2022introduction,
  title={An introduction to neural data compression},
  author={Yang, Yibo and Mandt, Stephan and Theis, Lucas},
  journal={arXiv preprint arXiv:2202.06533},
  year={2022}
}

@article{dupont2022coin++,
  title={Coin++: Data agnostic neural compression},
  author={Dupont, Emilien and Loya, Hrushikesh and Alizadeh, Milad and Goli{\'n}ski, Adam and Teh, Yee Whye and Doucet, Arnaud},
  journal={arXiv preprint arXiv:2201.12904},
  year={2022}
}

@inproceedings{sushmit2019x,
  title={X-ray image compression using convolutional recurrent neural networks},
  author={Sushmit, Asif Shahriyar and Zaman, Shakib Uz and Humayun, Ahmed Imtiaz and Hasan, Taufiq and Bhuiyan, Mohammed Imamul Hassan},
  booktitle={2019 IEEE EMBS International Conference on Biomedical \& Health Informatics (BHI)},
  pages={1--4},
  year={2019},
  organization={IEEE}
}

@article{panagiotidis1996neural,
  title={Neural network-assisted effective lossy compression of medical images},
  author={Panagiotidis, Nikos G and Kalogeras, Dimitris and Kollias, Stefanos D and Stafylopatis, Andreas},
  journal={Proceedings of the IEEE},
  volume={84},
  number={10},
  pages={1474--1487},
  year={1996},
  publisher={IEEE}
}

@article{tellez2019neural,
  title={Neural image compression for gigapixel histopathology image analysis},
  author={Tellez, David and Litjens, Geert and van der Laak, Jeroen and Ciompi, Francesco},
  journal={IEEE transactions on pattern analysis and machine intelligence},
  volume={43},
  number={2},
  pages={567--578},
  year={2019},
  publisher={IEEE}
}

@inproceedings{aswolinskiy2021neural,
  title={Neural image compression for non-small cell lung cancer subtype classification in H\&E stained whole-slide images},
  author={Aswolinskiy, Witali and Tellez, David and Raya, Gabriel and van der Woude, Lieke and Looijen-Salamon, Monika and van der Laak, Jeroen and Grunberg, Katrien and Ciompi, Francesco},
  booktitle={Medical Imaging 2021: Digital Pathology},
  volume={11603},
  pages={1160304},
  year={2021},
  organization={SPIE}
}

@inproceedings{tellez2020extending,
  title={Extending unsupervised neural image compression with supervised multitask learning},
  author={Tellez, David and H{\"o}ppener, Diederik and Verhoef, Cornelis and Gr{\"u}nhagen, Dirk and Nierop, Pieter and Drozdzal, Michal and Laak, Jeroen and Ciompi, Francesco},
  booktitle={Medical Imaging with Deep Learning},
  pages={770--783},
  year={2020},
  organization={PMLR}
}

@article{dai2019diagnosing,
  title={Diagnosing and enhancing VAE models},
  author={Dai, Bin and Wipf, David},
  journal={arXiv preprint arXiv:1903.05789},
  year={2019}
}

% Datasets
% Emory

% CBIS-DDSM (segmentation labels) -- scanned film mammography
@article{lee2017curated,
  title={A curated mammography data set for use in computer-aided detection and diagnosis research},
  author={Lee, Rebecca Sawyer and Gimenez, Francisco and Hoogi, Assaf and Miyake, Kanae Kawai and Gorovoy, Mia and Rubin, Daniel L},
  journal={Scientific data},
  volume={4},
  number={1},
  pages={1--9},
  year={2017},
  publisher={Nature Publishing Group}
}


@article{kuznetsova2020open,
  title={The open images dataset v4: Unified image classification, object detection, and visual relationship detection at scale},
  author={Kuznetsova, Alina and Rom, Hassan and Alldrin, Neil and Uijlings, Jasper and Krasin, Ivan and Pont-Tuset, Jordi and Kamali, Shahab and Popov, Stefan and Malloci, Matteo and Kolesnikov, Alexander and others},
  journal={International Journal of Computer Vision},
  volume={128},
  number={7},
  pages={1956--1981},
  year={2020},
  publisher={Springer}
}

@article{feng2021curation,
  title={Curation of the candid-ptx dataset with free-text reports},
  author={Feng, Sijing and Azzollini, Damian and Kim, Ji Soo and Jin, Cheng-Kai and Gordon, Simon P and Yeoh, Jason and Kim, Eve and Han, Mina and Lee, Andrew and Patel, Aakash and others},
  journal={Radiology: Artificial Intelligence},
  volume={3},
  number={6},
  pages={e210136},
  year={2021},
  publisher={Radiological Society of North America}
}

@article{jeong2023emory,
  title={The EMory BrEast imaging Dataset (EMBED): A racially diverse, granular dataset of 3.4 million screening and diagnostic mammographic images},
  author={Jeong, Jiwoong J and Vey, Brianna L and Bhimireddy, Ananth and Kim, Thomas and Santos, Thiago and Correa, Ramon and Dutt, Raman and Mosunjac, Marina and Oprea-Ilies, Gabriela and Smith, Geoffrey and others},
  journal={Radiology: Artificial Intelligence},
  volume={5},
  number={1},
  pages={e220047},
  year={2023},
  publisher={Radiological Society of North America}
}

@article{nguyen2022vindrcxr,
  title={VinDr-CXR: An open dataset of chest X-rays with radiologist’s annotations},
  author={Nguyen, Ha Q and others},
  journal={Scientific Data},
  volume={9},
  number={1},
  pages={429},
  year={2022},
  publisher={Nature Publishing Group UK London}
}


@article{chambon2022roentgen,
  title={RoentGen: Vision-Language Foundation Model for Chest X-ray Generation},
  author={Chambon, Pierre and Bluethgen, Christian and Delbrouck, Jean-Benoit and Van der Sluijs, Rogier and Po{\l}acin, Ma{\l}gorzata and Chaves, Juan Manuel Zambrano and Abraham, Tanishq Mathew and Purohit, Shivanshu and Langlotz, Curtis P and Chaudhari, Akshay},
  journal={arXiv preprint arXiv:2211.12737},
  year={2022}
}

% 22347940 
@Article{europeanrad2011,
   Author={European Society of Radiology,"},
   Title="{{U}sability of irreversible image compression in radiological imaging. {A} position paper by the {E}uropean {S}ociety of {R}adiology ({E}{S}{R})}",
   Journal="Insights Imaging",
   Year="2011",
   Volume="2",
   Number="2",
   Pages="103--115",
   Month="Apr"
}


@article{wallace1992jpeg,
  title={The JPEG still picture compression standard},
  author={Wallace, Gregory K},
  journal={IEEE transactions on consumer electronics},
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  number={1},
  pages={xviii--xxxiv},
  year={1992},
  publisher={IEEE}
}

@inproceedings{dodge2016understanding,
  title={Understanding how image quality affects deep neural networks},
  author={Dodge, Samuel and Karam, Lina},
  booktitle={2016 eighth international conference on quality of multimedia experience (QoMEX)},
  pages={1--6},
  year={2016},
  organization={IEEE}
}

@inproceedings{rezende2014stochastic,
  title={Stochastic backpropagation and approximate inference in deep generative models},
  author={Rezende, Danilo Jimenez and Mohamed, Shakir and Wierstra, Daan},
  booktitle={International conference on machine learning},
  pages={1278--1286},
  year={2014},
  organization={PMLR}
}

@article{shannon1948mathematical,
  title={A mathematical theory of communication},
  author={Shannon, Claude E},
  journal={The Bell system technical journal},
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  number={3},
  pages={379--423},
  year={1948},
  publisher={Nokia Bell Labs}
}

@inproceedings{szegedy2015inception,
  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{radford2021clip,
  title={Learning transferable visual models from natural language supervision},
  author={Radford, Alec and Kim, Jong Wook and Hallacy, Chris and Ramesh, Aditya and Goh, Gabriel and Agarwal, Sandhini and Sastry, Girish and Askell, Amanda and Mishkin, Pamela and Clark, Jack and others},
  booktitle={International conference on machine learning},
  pages={8748--8763},
  year={2021},
  organization={PMLR}
}

@inproceedings{zhang2016probe,
  title={Colorful image colorization},
  author={Zhang, Richard and Isola, Phillip and Efros, Alexei A},
  booktitle={Computer Vision--ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part III 14},
  pages={649--666},
  year={2016},
  organization={Springer}
}

@article{kingma2014adam,
  title={Adam: A method for stochastic optimization},
  author={Kingma, Diederik P and Ba, Jimmy},
  journal={arXiv preprint arXiv:1412.6980},
  year={2014}
}


@inproceedings{dubois2021lossy,
 author = {Dubois, Yann and Bloem-Reddy, Benjamin and Ullrich, Karen and Maddison, Chris J},
 booktitle = {Advances in Neural Information Processing Systems},
 editor = {M. Ranzato and A. Beygelzimer and Y. Dauphin and P.S. Liang and J. Wortman Vaughan},
 pages = {14014--14028},
 publisher = {Curran Associates, Inc.},
 title = {Lossy Compression for Lossless Prediction},
 url = {https://proceedings.neurips.cc/paper_files/paper/2021/file/7535bbb91c8fde347ad861f293126633-Paper.pdf},
 volume = {34},
 year = {2021}
}

@article{guan2020fracture,
title = {Arm fracture detection in X-rays based on improved deep convolutional neural network},
journal = {Computers & Electrical Engineering},
volume = {81},
pages = {106530},
year = {2020},
issn = {0045-7906},
doi = {https://doi.org/10.1016/j.compeleceng.2019.106530},
url = {https://www.sciencedirect.com/science/article/pii/S0045790618330878},
author = {Bin Guan and Guoshan Zhang and Jinkun Yao and Xinbo Wang and Mengxuan Wang},
}

@article{Naranje2016frac,
  doi = {10.1097/bpo.0000000000000595},
  url = {https://doi.org/10.1097/bpo.0000000000000595},
  year = {2016},
  month = jun,
  publisher = {Ovid Technologies (Wolters Kluwer Health)},
  volume = {36},
  number = {4},
  pages = {e45--e48},
  author = {Sameer M. Naranje and Richard A. Erali and William C. Warner and Jeffrey R. Sawyer and Derek M. Kelly},
  title = {Epidemiology of Pediatric Fractures Presenting to Emergency Departments in the United States},
  journal = {Journal of Pediatric Orthopaedics}
}

@article{DeSanctis2014boneage,
  doi = {10.4103/2230-8210.145076},
  url = {https://doi.org/10.4103/2230-8210.145076},
  year = {2014},
  publisher = {Medknow},
  volume = {18},
  number = {7},
  pages = {63},
  author = {Vincenzo De Sanctis and Salvatore Di Maio and AshrafT Soliman and Giuseppe Raiola and Rania Elalaily and Giuseppe Millimaggi},
  title = {Hand X-ray in pediatric endocrinology: Skeletal age assessment and beyond},
  journal = {Indian Journal of Endocrinology and Metabolism}
}

@incollection{bassettbreast,
title = {26 - Breast Imaging Screening and Diagnosis},
editor = {Kirby I. Bland and Edward M. Copeland and V. Suzanne Klimberg and William J. Gradishar},
booktitle = {The Breast (Fifth Edition)},
publisher = {Elsevier},
edition = {Fifth Edition},
pages = {337-361.e2},
year = {2018},
isbn = {978-0-323-35955-9},
doi = {https://doi.org/10.1016/B978-0-323-35955-9.00026-X},
url = {https://www.sciencedirect.com/science/article/pii/B978032335955900026X},
author = {Lawrence W. Bassett and Stephanie Lee-Felker},
keywords = {screening mammography, diagnostic mammography, breast ultrasound, breast magnetic resonance imaging, core needle biopsy, preoperative needle localization},
abstract = {This chapter reviews the most commonly used imaging modalities in the screening and diagnosis of breast diseases. Topics discussed include characteristic benign and malignant imaging findings on mammography, breast ultrasound, and breast magnetic resonance imaging, as well as common image-guided breast interventional procedures, including core needle biopsy and preoperative needle localization.}
}

@inproceedings{pytorch,
 author = {Paszke, Adam and Gross, Sam and Massa, Francisco and Lerer, Adam and Bradbury, James and Chanan, Gregory and Killeen, Trevor and Lin, Zeming and Gimelshein, Natalia and Antiga, Luca and Desmaison, Alban and Kopf, Andreas and Yang, Edward and DeVito, Zachary and Raison, Martin and Tejani, Alykhan and Chilamkurthy, Sasank and Steiner, Benoit and Fang, Lu and Bai, Junjie and Chintala, Soumith},
 booktitle = {Advances in Neural Information Processing Systems},
 editor = {H. Wallach and H. Larochelle and A. Beygelzimer and F. d\textquotesingle Alch\'{e}-Buc and E. Fox and R. Garnett},
 pages = {},
 publisher = {Curran Associates, Inc.},
 title = {PyTorch: An Imperative Style, High-Performance Deep Learning Library},
 url = {https://proceedings.neurips.cc/paper_files/paper/2019/file/bdbca288fee7f92f2bfa9f7012727740-Paper.pdf},
 volume = {32},
 year = {2019}
}

@misc{miura2021improving,
      title={Improving Factual Completeness and Consistency of Image-to-Text Radiology Report Generation}, 
      author={Yasuhide Miura and Yuhao Zhang and Emily Bao Tsai and Curtis P. Langlotz and Dan Jurafsky},
      year={2021},
      eprint={2010.10042},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

@article{Tiu2022,
  doi = {10.1038/s41551-022-00936-9},
  url = {https://doi.org/10.1038/s41551-022-00936-9},
  year = {2022},
  month = sep,
  publisher = {Springer Science and Business Media {LLC}},
  volume = {6},
  number = {12},
  pages = {1399--1406},
  author = {Ekin Tiu and Ellie Talius and Pujan Patel and Curtis P. Langlotz and Andrew Y. Ng and Pranav Rajpurkar},
  title = {Expert-level detection of pathologies from unannotated chest X-ray images via self-supervised learning},
  journal = {Nature Biomedical Engineering}
}

@ARTICLE{Liu4K,
      author={J. {Liu} and D. {Liu} and W. {Yang} and S. {Xia} and X. {Zhang} and Y. {Dai}},
      journal={IEEE Transactions on Image Processing}, 
      title={A Comprehensive Benchmark for Single Image Compression Artifact Reduction}, 
      year={2020},
      volume={29},
      number={},
      pages={7845-7860},
}

@misc{msssim,
      title={Learning to Generate Images with Perceptual Similarity Metrics}, 
      author={Jake Snell and Karl Ridgeway and Renjie Liao and Brett D. Roads and Michael C. Mozer and Richard S. Zemel},
      year={2017},
      eprint={1511.06409},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

@misc{patel2019human,
      title={Human Perceptual Evaluations for Image Compression}, 
      author={Yash Patel and Srikar Appalaraju and R. Manmatha},
      year={2019},
      eprint={1908.04187},
      archivePrefix={arXiv},
      primaryClass={eess.IV}
}

@misc{zhang2018unreasonable,
      title={The Unreasonable Effectiveness of Deep Features as a Perceptual Metric}, 
      author={Richard Zhang and Phillip Isola and Alexei A. Efros and Eli Shechtman and Oliver Wang},
      year={2018},
      archivePrefix={CVPR},
}

@InProceedings{psnr,
author="Kotevski, Zoran
and Mitrevski, Pece",
editor="Davcev, Danco
and G{\'o}mez, Jorge Marx",
title="Experimental Comparison of PSNR and SSIM Metrics for Video Quality Estimation",
booktitle="ICT Innovations 2009",
year="2010",
publisher="Springer Berlin Heidelberg",
address="Berlin, Heidelberg",
pages="357--366",
abstract="Since the development of digital video technology, due to the nature of digital video, the approach to video quality estimation has changed. Basically there are two types of metrics used to measure the objective quality of processed digital video: purely mathematically defined video quality metrics (DELTA, MSAD, MSE, SNR and PSNR) where the error is mathematically calculated as a difference between the original and processed pixel, and video quality metrics that have similar characteristics as the Human Visual System -- HVS (SSIM, NQI, VQM) where the perceptual quality is considered in the overall video quality estimation. In this paper, an overview and experimental comparison of PSNR and SSIM metrics for video quality estimation is presented.",
isbn="978-3-642-10781-8"
}

@misc{park2023rgb,
      title={RGB no more: Minimally-decoded JPEG Vision Transformers}, 
      author={Jeongsoo Park and Justin Johnson},
      year={2023},
      archivePrefix={CVPR},
}

@misc{wiles2022compressed,
      title={Compressed Vision for Efficient Video Understanding}, 
      author={Olivia Wiles and Joao Carreira and Iain Barr and Andrew Zisserman and Mateusz Malinowski},
      year={2022},
      eprint={2210.02995},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

@misc{ronneberger2015unet,
      title={U-Net: Convolutional Networks for Biomedical Image Segmentation}, 
      author={Olaf Ronneberger and Philipp Fischer and Thomas Brox},
      year={2015},
      archivePrefix={MICCAI},
}

@misc{bakas2019identifying,
      title={Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge}, 
      author={Spyridon Bakas and Mauricio Reyes and Andras Jakab and Stefan Bauer and others},
      year={2019},
      eprint={1811.02629},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}
@Article{Bakas2017,
author={Bakas, Spyridon
and Akbari, Hamed
and Sotiras, Aristeidis
and Bilello, Michel
and Rozycki, Martin
and Kirby, Justin S.
and Freymann, John B.
and Farahani, Keyvan
and Davatzikos, Christos},
title={Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features},
journal={Scientific Data},
year={2017},
month={Sep},
day={05},
volume={4},
number={1},
pages={170117},
issn={2052-4463},
doi={10.1038/sdata.2017.117},
url={https://doi.org/10.1038/sdata.2017.117}
}


@ARTICLE{menze2015,
  author={Menze, Bjoern H. and Jakab, Andras and Bauer, Stefan and Kalpathy-Cramer, Jayashree and others},
  journal={IEEE Transactions on Medical Imaging}, 
  title={The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)}, 
  year={2015},
  volume={34},
  number={10},
  pages={1993-2024},
  doi={10.1109/TMI.2014.2377694}}


@article{begaint2020compressai,
	title={CompressAI: a PyTorch library and evaluation platform for end-to-end compression research},
	author={B{\'e}gaint, Jean and Racap{\'e}, Fabien and Feltman, Simon and Pushparaja, Akshay},
	year={2020},
	journal={arXiv preprint arXiv:2011.03029},
}

@misc{ballé2018hyperprior,
      title={Variational image compression with a scale hyperprior}, 
      author={Johannes Ballé and David Minnen and Saurabh Singh and Sung Jin Hwang and Nick Johnston},
      year={2018},
      archivePrefix={International Conference on Learning Representations},
}

@misc{cheng2020learned,
      title={Learned Image Compression with Discretized Gaussian Mixture Likelihoods and Attention Modules}, 
      author={Zhengxue Cheng and Heming Sun and Masaru Takeuchi and Jiro Katto},
      year={2020},
      archivePrefix={CVPR 2020},
      primaryClass={eess.IV}
}

@misc{chen2020simclr,
      title={A Simple Framework for Contrastive Learning of Visual Representations}, 
      author={Ting Chen and Simon Kornblith and Mohammad Norouzi and Geoffrey Hinton},
      year={2020},
      eprint={2002.05709},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

@article{bilinear,
title = {Bilinear interpolation of digital images},
journal = {Ultramicroscopy},
volume = {6},
number = {2},
pages = {201-204},
year = {1981},
issn = {0304-3991},
doi = {https://doi.org/10.1016/0304-3991(81)90061-9},
url = {https://www.sciencedirect.com/science/article/pii/0304399181900619},
author = {P.R. Smith},
abstract = {The application of the method of three-point bilinear interpolation is shown to generate a smoothly interpolated image, free from erroneous substructure generated by the interpolation scheme itself. Three-point interpolation is therefore to be preferred to the standard four-point bilinear scheme when images are prepared for film writing.}
}

@article{superressurvey,
title = {Image super-resolution survey},
journal = {Image and Vision Computing},
volume = {24},
number = {10},
pages = {1039-1052},
year = {2006},
issn = {0262-8856},
doi = {https://doi.org/10.1016/j.imavis.2006.02.026},
url = {https://www.sciencedirect.com/science/article/pii/S0262885606001089},
author = {J.D. {van Ouwerkerk}},
keywords = {Super-resolution, Single frame},
abstract = {The shortcomings in commonly used kernel-based super-resolution drive the study of improved super-resolution algorithms of higher quality. In the past years a wide range of very different approaches has been taken to improve super-resolution. This paper compares approaches to high quality super-resolution by looking at theoretical backgrounds and practical results. Strengths and weaknesses are listed with the intent to spot chances for combination or improvement of techniques, thereby forming a base for future improved super-resolution algorithms.}
}

@inproceedings{
adamson2023using,
title={Using Deep Feature Distances for Evaluating {MR} Image Reconstruction Quality},
author={Philip M Adamson and Arjun D Desai and Jeffrey Dominic and Christian Bluethgen and Jeff P. Wood and Ali B Syed and Robert D. Boutin and Kathryn J. Stevens and Shreyas Vasanawala and John M. Pauly and Akshay S Chaudhari and Beliz Gunel},
booktitle={NeurIPS 2023 Workshop on Deep Learning and Inverse Problems},
year={2023},
url={https://openreview.net/forum?id=AUiZyqYiGb}
}


@Article{csawdata,
author={Strand, Fredrik},
title={CSAW-CC (mammography) – a dataset for AI research to improve screening, diagnostics and prognostics of breast cancer},
year={2022},
journal={Swedish National Data Service},
doi={https://doi.org/10.5878/45vm-t798},
url={https://doi.org/10.5878/45vm-t798}
}

@misc{timm,
  author = {Ross Wightman},
  title = {PyTorch Image Models},
  year = {2019},
  publisher = {GitHub},
  journal = {GitHub repository},
  doi = {10.5281/zenodo.4414861},
  howpublished = {\url{https://github.com/rwightman/pytorch-image-models}}
}

@article{PhysioNet,
 author = "Goldberger, A. L. and Amaral, L. A. N. and Glass, L. and
	   Hausdorff, J. M. and Ivanov, P. Ch. and Mark, R. G. and
	   Mietus, J. E. and Moody, G. B. and Peng, C.-K. and Stanley, H. E.",
 title = "{PhysioBank, PhysioToolkit, and PhysioNet}: Components of a New
	  Research Resource for Complex Physiologic Signals",
 journal = "Circulation",
 year = "2000 (June 13)",
 volume = "101",
 number = "23",
 pages = "e215--e220",
 note = "Circulation Electronic Pages:
         http://circ.ahajournals.org/content/101/23/e215.full
         PMID:1085218; doi: 10.1161/01.CIR.101.23.e215"}







@article{larson,
author = {Larson, David                            B. and Chen, Matthew                            C. and Lungren, Matthew                            P. and Halabi, Safwan                            S. and Stence, Nicholas                            V. and Langlotz, Curtis                            P.},
title = {Performance of a Deep-Learning Neural Network Model in Assessing                    Skeletal Maturity on Pediatric Hand Radiographs},
journal = {Radiology},
volume = {287},
number = {1},
pages = {313-322},
year = {2018},
doi = {10.1148/radiol.2017170236},
    note ={PMID: 29095675},

URL = { 
    
        https://doi.org/10.1148/radiol.2017170236
    
    

},
eprint = { 
    
        https://doi.org/10.1148/radiol.2017170236
    
    

}
,
}


@misc{adamw,
      title={Decoupled Weight Decay Regularization}, 
      author={Ilya Loshchilov and Frank Hutter},
      year={2019},
      eprint={1711.05101},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

@misc{ronneberger2015unet,
      title={U-Net: Convolutional Networks for Biomedical Image Segmentation}, 
      author={Olaf Ronneberger and Philipp Fischer and Thomas Brox},
      year={2015},
      archivePrefix={MICCAI},
}

@misc{bakas2019identifying,
      title={Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge}, 
      author={Spyridon Bakas and Mauricio Reyes and Andras Jakab and Stefan Bauer and others},
      year={2019},
      eprint={1811.02629},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}
@Article{Bakas2017,
author={Bakas, Spyridon
and Akbari, Hamed
and Sotiras, Aristeidis
and Bilello, Michel
and Rozycki, Martin
and Kirby, Justin S.
and Freymann, John B.
and Farahani, Keyvan
and Davatzikos, Christos},
title={Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features},
journal={Scientific Data},
year={2017},
month={Sep},
day={05},
volume={4},
number={1},
pages={170117},
issn={2052-4463},
doi={10.1038/sdata.2017.117},
url={https://doi.org/10.1038/sdata.2017.117}
}


@ARTICLE{menze2015,
  author={Menze, Bjoern H. and Jakab, Andras and Bauer, Stefan and Kalpathy-Cramer, Jayashree and others},
  journal={IEEE Transactions on Medical Imaging}, 
  title={The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)}, 
  year={2015},
  volume={34},
  number={10},
  pages={1993-2024},
  doi={10.1109/TMI.2014.2377694}}

@article{begaint2020compressai,
	title={CompressAI: a PyTorch library and evaluation platform for end-to-end compression research},
	author={B{\'e}gaint, Jean and Racap{\'e}, Fabien and Feltman, Simon and Pushparaja, Akshay},
	year={2020},
	journal={arXiv preprint arXiv:2011.03029},
}

@misc{ballé2018variational,
      title={Variational image compression with a scale hyperprior}, 
      author={Johannes Ballé and David Minnen and Saurabh Singh and Sung Jin Hwang and Nick Johnston},
      year={2018},
      archivePrefix={International Conference on Learning Representations},
}

@misc{cheng2020learned,
      title={Learned Image Compression with Discretized Gaussian Mixture Likelihoods and Attention Modules}, 
      author={Zhengxue Cheng and Heming Sun and Masaru Takeuchi and Jiro Katto},
      year={2020},
      archivePrefix={CVPR 2020},
      primaryClass={eess.IV}
}

@article{sekuboyina2021verse,
title = {VerSe: A Vertebrae labelling and segmentation benchmark for multi-detector CT images},
journal = {Medical Image Analysis},
volume = {73},
pages = {102166},
year = {2021},
issn = {1361-8415},
doi = {https://doi.org/10.1016/j.media.2021.102166},
url = {https://www.sciencedirect.com/science/article/pii/S1361841521002127},
keywords = {Spine, Vertebrae, Segmentation, Labelling},
}


@Article{tcia,
author={Clark, Kenneth
and Vendt, Bruce
and Smith, Kirk
and Freymann, John
and Kirby, Justin
and Koppel, Paul
and Moore, Stephen
and Phillips, Stanley
and Maffitt, David
and Pringle, Michael
and Tarbox, Lawrence
and Prior, Fred},
title={The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository},
journal={Journal of Digital Imaging},
year={2013},
month={Dec},
day={01},
volume={26},
number={6},
pages={1045-1057},
issn={1618-727X},
doi={10.1007/s10278-013-9622-7},
url={https://doi.org/10.1007/s10278-013-9622-7}
}


@Article{cmmddata,
author={Cui, Chunyan and Li, Li and Cai, Hongmin and Fan, Zhihao and Zhang, Ling and Dan, Tingting and Li, Jiao and Wang, Jinghua},
title={The Chinese Mammography Database (CMMD): An online mammography database with biopsy confirmed types for machine diagnosis of breast},
year={2021},
journal={The Cancer Imaging Archive},
doi={https://doi.org/10.7937/tcia.eqde-4b16},
url={https://doi.org/10.7937/tcia.eqde-4b16}
}

@misc{khader2023medical,
      title={Medical Diffusion: Denoising Diffusion Probabilistic Models for 3D Medical Image Generation}, 
      author={Firas Khader and Gustav Mueller-Franzes and Soroosh Tayebi Arasteh and Tianyu Han and Christoph Haarburger and Maximilian Schulze-Hagen and Philipp Schad and Sandy Engelhardt and Bettina Baessler and Sebastian Foersch and Johannes Stegmaier and Christiane Kuhl and Sven Nebelung and Jakob Nikolas Kather and Daniel Truhn},
      year={2023},
      eprint={2211.03364},
      archivePrefix={arXiv},
      primaryClass={eess.IV}
}


@article{chen2020big,
  title={Big self-supervised models are strong semi-supervised learners},
  author={Chen, Ting and Kornblith, Simon and Swersky, Kevin and Norouzi, Mohammad and Hinton, Geoffrey E},
  journal={Advances in neural information processing systems},
  volume={33},
  pages={22243--22255},
  year={2020}
}

@article{holland2022metadata,
  title={Metadata-enhanced contrastive learning from retinal optical coherence tomography images},
  author={Holland, Robbie and Leingang, Oliver and Bogunovi{\'c}, Hrvoje and Riedl, Sophie and Fritsche, Lars and Prevost, Toby and Scholl, Hendrik PN and Schmidt-Erfurth, Ursula and Sivaprasad, Sobha and Lotery, Andrew J and others},
  journal={arXiv preprint arXiv:2208.02529},
  year={2022}
}

@article{kwak2023self,
  title={Self-Supervised Contrastive Learning to Predict the Progression of Alzheimer’s Disease with 3D Amyloid-PET},
  author={Kwak, Min Gu and Su, Yi and Chen, Kewei and Weidman, David and Wu, Teresa and Lure, Fleming and Li, Jing and Alzheimer’s Disease Neuroimaging Initiative},
  journal={Bioengineering},
  volume={10},
  number={10},
  pages={1141},
  year={2023},
  publisher={MDPI}
}

@misc{chen2024chexagentfoundationmodelchest,
      title={CheXagent: Towards a Foundation Model for Chest X-Ray Interpretation}, 
      author={Zhihong Chen and others},
      year={2024},
      eprint={2401.12208},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2401.12208}, 
}


@article{Zhou2023,
  title = {A foundation model for generalizable disease detection from retinal images},
  volume = {622},
  ISSN = {1476-4687},
  url = {http://dx.doi.org/10.1038/s41586-023-06555-x},
  DOI = {10.1038/s41586-023-06555-x},
  number = {7981},
  journal = {Nature},
  publisher = {Springer Science and Business Media LLC},
  author = {Zhou,  Yukun and others},
  year = {2023},
  month = sep,
  pages = {156–163}
}

@misc{li2023llavamedtraininglargelanguageandvision,
      title={LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Day}, 
      author={Chunyuan Li and others},
      year={2023},
      eprint={2306.00890},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2306.00890}, 
}

@article{sabottke2020effect,
  title={The effect of image resolution on deep learning in radiography},
  author={Sabottke, Carl F and Spieler, Bradley M},
  journal={Radiology: Artificial Intelligence},
  volume={2},
  number={1},
  pages={e190015},
  year={2020},
  publisher={Radiological Society of North America}
}

@article{huang2023self,
  title={Self-supervised learning for medical image classification: a systematic review and implementation guidelines},
  author={Huang, Shih-Cheng and Pareek, Anuj and Jensen, Malte and Lungren, Matthew P and Yeung, Serena and Chaudhari, Akshay S},
  journal={NPJ Digital Medicine},
  volume={6},
  number={1},
  pages={74},
  year={2023},
  publisher={Nature Publishing Group UK London}
}

@misc{merlin,
      title={Merlin: A Vision Language Foundation Model for 3D Computed Tomography}, 
      author={Louis Blankemeier and others},
      year={2024},
      eprint={2406.06512},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2406.06512}, 
}