%%% Datasets 
%% UKB
@article{UKB,
   author = {Cathie Sudlow and John Gallacher and Naomi Allen and Valerie Beral and Paul Burton and John Danesh and Paul Downey and Paul Elliott and Jane Green and Martin Landray and Bette Liu and Paul Matthews and Giok Ong and Jill Pell and Alan Silman and Alan Young and Tim Sprosen and Tim Peakman and Rory Collins},
   doi = {10.1371/JOURNAL.PMED.1001779},
   issn = {15491676},
   issue = {3},
   journal = {PLoS Medicine},
   month = {3},
   pmid = {25826379},
   publisher = {Public Library of Science},
   title = {UK Biobank: An Open Access Resource for Identifying the Causes of a Wide Range of Complex Diseases of Middle and Old Age},
   volume = {12},
   year = {2015},
}
%% ADNI
@ARTICLE{adni_Petersen2010-zh,
  title     = "Alzheimer's Disease Neuroimaging Initiative ({ADNI)}: clinical
               characterization",
  author    = "Petersen, R C and Aisen, P S and Beckett, L A and Donohue, M C
               and Gamst, A C and Harvey, D J and Jack, Jr, C R and Jagust, W J
               and Shaw, L M and Toga, A W and Trojanowski, J Q and Weiner, M W",
  abstract  = "BACKGROUND: Neuroimaging measures and chemical biomarkers may be
               important indices of clinical progression in normal aging and
               mild cognitive impairment (MCI) and need to be evaluated
               longitudinally. OBJECTIVE: To characterize cross-sectionally and
               longitudinally clinical measures in normal controls, subjects
               with MCI, and subjects with mild Alzheimer disease (AD) to
               enable the assessment of the utility of neuroimaging and
               chemical biomarker measures. METHODS: A total of 819 subjects
               (229 cognitively normal, 398 with MCI, and 192 with AD) were
               enrolled at baseline and followed for 12 months using standard
               cognitive and functional measures typical of clinical trials.
               RESULTS: The subjects with MCI were more memory impaired than
               the cognitively normal subjects but not as impaired as the
               subjects with AD. Nonmemory cognitive measures were only
               minimally impaired in the subjects with MCI. The subjects with
               MCI progressed to dementia in 12 months at a rate of 16.5\% per
               year. Approximately 50\% of the subjects with MCI were on
               antidementia therapies. There was minimal movement on the
               Alzheimer's Disease Assessment Scale-Cognitive Subscale for the
               normal control subjects, slight movement for the subjects with
               MCI of 1.1, and a modest change for the subjects with AD of 4.3.
               Baseline CSF measures of Abeta-42 separated the 3 groups as
               expected and successfully predicted the 12-month change in
               cognitive measures. CONCLUSION: The Alzheimer's Disease
               Neuroimaging Initiative has successfully recruited cohorts of
               cognitively normal subjects, subjects with mild cognitive
               impairment (MCI), and subjects with Alzheimer disease with
               anticipated baseline characteristics. The 12-month progression
               rate of MCI was as predicted, and the CSF measures heralded
               progression of clinical measures over 12 months.",
  journal   = "Neurology",
  publisher = "American Academy of Neurology",
  volume    =  74,
  number    =  3,
  pages     = "201--209",
  month     =  jan,
  year      =  2010,
  keywords  = "Aged; Aged, 80 and over; Alzheimer
               Disease/diagnosis/*metabolism/*pathology; Biomarkers/metabolism;
               Cross-Sectional Studies; Diagnostic Imaging/*standards; Female;
               Follow-Up Studies; Humans; Longitudinal Studies; Male; Middle
               Aged"
}
%% Morpho-MNIST
@article{castro2019morphomnist,
    author = {Castro, Daniel C. and Tan, Jeremy and Kainz, Bernhard and Konukoglu, Ender and Glocker, Ben},
    title = {{Morpho-MNIST}: Quantitative Assessment and Diagnostics for Representation Learning},
    year = {2019},
    journal = {Journal of Machine Learning Research},
    volume = {20},
    number = {178},
    eprint = {arXiv:1809.10780}
}
%% Eyepacs
@article{eyepacs,
author = {Cuadros, Jorge and Bresnick, George},
year = {2009},
month = {05},
pages = {509-16},
title = {EyePACS: An Adaptable Telemedicine System for Diabetic Retinopathy Screening},
volume = {3},
journal = {Journal of diabetes science and technology},
doi = {10.1177/193229680900300315}
}
%% rfmid
@Article{rfmid,
AUTHOR = {Pachade, Samiksha and Porwal, Prasanna and Thulkar, Dhanshree and Kokare, Manesh and Deshmukh, Girish and Sahasrabuddhe, Vivek and Giancardo, Luca and Quellec, Gwenolé and Mériaudeau, Fabrice},
TITLE = {Retinal Fundus Multi-Disease Image Dataset (RFMiD): A Dataset for Multi-Disease Detection Research},
JOURNAL = {Data},
VOLUME = {6},
YEAR = {2021},
NUMBER = {2},
ARTICLE-NUMBER = {14},
URL = {https://www.mdpi.com/2306-5729/6/2/14},
ISSN = {2306-5729},
ABSTRACT = {The world faces difficulties in terms of eye care, including treatment, quality of prevention, vision rehabilitation services, and scarcity of trained eye care experts. Early detection and diagnosis of ocular pathologies would enable forestall of visual impairment. One challenge that limits the adoption of computer-aided diagnosis tool by ophthalmologists is the number of sight-threatening rare pathologies, such as central retinal artery occlusion or anterior ischemic optic neuropathy, and others are usually ignored. In the past two decades, many publicly available datasets of color fundus images have been collected with a primary focus on diabetic retinopathy, glaucoma, age-related macular degeneration and few other frequent pathologies. To enable development of methods for automatic ocular disease classification of frequent diseases along with the rare pathologies, we have created a new Retinal Fundus Multi-disease Image Dataset (RFMiD). It consists of 3200 fundus images captured using three different fundus cameras with 46 conditions annotated through adjudicated consensus of two senior retinal experts. To the best of our knowledge, our dataset, RFMiD, is the only publicly available dataset that constitutes such a wide variety of diseases that appear in routine clinical settings. This dataset will enable the development of generalizable models for retinal screening.},
DOI = {10.3390/data6020014}
}
%% age and grey volumes
@ARTICLE{gm_cognitive_aging,
AUTHOR={Ramanoël, Stephen and Hoyau, Elena and Kauffmann, Louise and Renard, Félix and Pichat, Cédric and Boudiaf, Naïla and Krainik, Alexandre and Jaillard, Assia and Baciu, Monica},   
TITLE={Gray Matter Volume and Cognitive Performance During Normal Aging. A Voxel-Based Morphometry Study},      	
JOURNAL={Frontiers in Aging Neuroscience},      
VOLUME={10},           
YEAR={2018},      
URL={https://www.frontiersin.org/articles/10.3389/fnagi.2018.00235},       
DOI={10.3389/fnagi.2018.00235},      
ISSN={1663-4365},
ABSTRACT={Normal aging is characterized by decline in cognitive functioning in conjunction with extensive gray matter (GM) atrophy. A first aim of this study was to determine GM volume differences related to aging by comparing two groups of participants, middle-aged group (MAG, mean age 41 years, N = 16) and older adults (OG, mean age 71 years, N = 14) who underwent an magnetic resonance images (MRI) voxel-based morphometry (VBM) evaluation. The VBM analyses included two optimized pipelines, for the cortex and for the cerebellum. Participants were also evaluated on a wide range of cognitive tests assessing both domain-general and language-specific processes, in order to examine how GM volume differences between OG and MAG relate to cognitive performance. Our results show smaller bilateral GM volume in the OG relative to the MAG, in several cerebral and right cerebellar regions involved in language and executive functions. Importantly, our results also revealed smaller GM volume in the right cerebellum in OG relative to MAG, supporting the idea of a complex cognitive role for this structure. This study provides a broad picture of cerebral, but also cerebellar and cognitive changes associated with normal aging.}
}

%% cognitive decline and gm
@article{Arrondo2022GreyMC,
  title={Grey matter changes on brain MRI in subjective cognitive decline: a systematic review},
  author={Pablo Arrondo and Oscar El{\'i}a-Zudaire and Gloria Mart{\'i}-Andr{\'e}s and Mar{\'i}a A. Fern{\'a}ndez-Seara and Mario Riverol},
  journal={Alzheimer's Research \& Therapy},
  year={2022},
  volume={14},
  url={https://api.semanticscholar.org/CorpusID:256060084}
}

@ARTICLE{age_related_gm_decline,
AUTHOR={Callaert, Dorothée V. and Ribbens, Annemie and Maes, Frederik and Swinnen, Stephan P. and Wenderoth, Nicole},   
TITLE={Assessing age-related gray matter decline with voxel-based morphometry depends significantly on segmentation and normalization procedures},      
JOURNAL={Frontiers in Aging Neuroscience},      
VOLUME={6},           
YEAR={2014},      
URL={https://www.frontiersin.org/articles/10.3389/fnagi.2014.00124},       
DOI={10.3389/fnagi.2014.00124},      	
ISSN={1663-4365},   
ABSTRACT={Healthy ageing coincides with a progressive decline of brain gray matter (GM) ultimately affecting the entire brain. For a long time, manual delineation-based volumetry within predefined regions of interest (ROI) has been the gold standard for assessing such degeneration. Voxel-Based Morphometry (VBM) offers an automated alternative approach that, however, relies critically on the segmentation and spatial normalization of a large collection of images from different subjects. This can be achieved via different algorithms, with SPM5/SPM8, DARTEL of SPM8 and FSL tools (FAST, FNIRT) being three of the most frequently used. We complemented these voxel based measurements with a ROI based approach, whereby the ROIs are defined by transforms of an atlas (containing different tissue probability maps as well as predefined anatomic labels) to the individual subject images in order to obtain volumetric information at the level of the whole brain or within separate ROIs. Comparing GM decline between 21 young subjects (mean age 23) and 18 elderly (mean age 66) revealed that volumetric measurements differed significantly between methods. The unified segmentation/normalization of SPM5/SPM8 revealed the largest age-related differences and DARTEL the smallest, with FSL being more similar to the DARTEL approach. Method specific differences were substantial after segmentation and most pronounced for the cortical structures in close vicinity to major sulci and fissures. Our findings suggest that algorithms that provide only limited degrees of freedom for local deformations (such as the unified segmentation and normalization of SPM5/SPM8) tend to overestimate between-group differences in VBM results when compared to methods providing more flexible warping. This difference seems to be most pronounced if the anatomy of one of the groups deviates from custom templates, a finding that is of particular importance when results are compared across studies using different VBM methods.}
}

%%% predicting ages from mri
@ARTICLE{age_mri_prediction,
AUTHOR={Ballester, Pedro L.  and da Silva, Laura Tomaz  and Marcon, Matheus  and Esper, Nathalia Bianchini  and Frey, Benicio N.  and Buchweitz, Augusto  and Meneguzzi, Felipe },

TITLE={Predicting Brain Age at Slice Level: Convolutional Neural Networks and Consequences for Interpretability},

JOURNAL={Frontiers in Psychiatry},

VOLUME={12},

YEAR={2021},

URL={https://www.frontiersin.org/journals/psychiatry/articles/10.3389/fpsyt.2021.598518},

DOI={10.3389/fpsyt.2021.598518},

ISSN={1664-0640},

ABSTRACT={<p><bold>Problem:</bold> Chronological aging in later life is associated with brain degeneration processes and increased risk for disease such as stroke and dementia. With a worldwide tendency of aging populations and increased longevity, mental health, and psychiatric research have paid increasing attention to understanding brain-related changes of aging. Recent findings suggest there is a <italic>brain age gap</italic> (a difference between chronological age and brain age predicted by brain imaging indices); the magnitude of the gap may indicate early onset of brain aging processes and disease. Artificial intelligence has allowed for a narrowing of the gap in chronological and predicted brain age. However, the factors that drive model predictions of brain age are still unknown, and there is not much about these factors that can be gleaned from the black-box nature of machine learning models. The goal of the present study was to test a brain age regression approach that is more amenable to interpretation by researchers and clinicians.</p><p><bold>Methods:</bold> Using convolutional neural networks we trained multiple regressor models to predict brain age based on single slices of magnetic resonance imaging, which included gray matter- or white matter-segmented inputs. We evaluated the trained models in all brain image slices to generate a final prediction of brain age. Unlike whole-brain approaches to classification, the slice-level predictions allows for the identification of which brain slices and associated regions have the largest difference between chronological and neuroimaging-derived brain age. We also evaluated how model predictions were influenced by slice index and plane, participant age and sex, and MRI data collection site.</p><p><bold>Results:</bold> The results show, first, that the specific slice used for prediction affects prediction error (i.e., difference between chronological age and neuroimaging-derived brain age); second, the MRI site-stratified separation of training and test sets removed site effects and also minimized sex effects; third, the choice of MRI slice plane influences the overall error of the model.</p><p><bold>Conclusion:</bold> Compared to whole brain-based predictive models of neuroimaging-derived brain age, slice-based approach improves the interpretability and therefore the reliability of the prediction of brain age using MRI data.</p>}
}

@inproceedings{cause_effect_learning,
author = {Sch\"{o}lkopf, Bernhard and Janzing, Dominik and Peters, Jonas and Sgouritsa, Eleni and Zhang, Kun and Mooij, Joris}, title = {On causal and anticausal learning}, year = {2012}, isbn = {9781450312851}, publisher = {Omnipress}, address = {Madison, WI, USA}, abstract = {We consider the problem of function estimation in the case where an underlying causal model can be inferred. This has implications for popular scenarios such as covariate shift, concept drift, transfer learning and semi-supervised learning. We argue that causal knowledge may facilitate some approaches for a given problem, and rule out others. In particular, we formulate a hypothesis for when semi-supervised learning can help, and corroborate it with empirical results.}, booktitle = {Proceedings of the 29th International Coference on International Conference on Machine Learning}, pages = {459–466}, numpages = {8}, location = {Edinburgh, Scotland}, series = {ICML'12} }

%%synthseg%
@article{billot_synthseg_2023,
	title = {SynthSeg: {Segmentation} of brain {MRI} scans of any contrast and resolution without retraining},
	author = {Billot, Benjamin and Greve, Douglas N. and Puonti, Oula and Thielscher, Axel and Van Leemput, Koen and Fischl, Bruce and Dalca, Adrian V. and Iglesias, Juan Eugenio},
	journal = {{Medical} {Image} {Analysis}},
	year = {2023},
    volume = {86},
	pages = {102789},
    issn = {1361-8415},
    doi = {10.1016/j.media.2023.102789},
}

%%%%%{rombach2021highresolution,
@misc{stablediffusion,
      title={High-Resolution Image Synthesis with Latent Diffusion Models}, 
      author={Robin Rombach and Andreas Blattmann and Dominik Lorenz and Patrick Esser and Björn Ommer},
      year={2021},
      eprint={2112.10752},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}
@inproceedings{diffusion_model_dickstein, 
    author = {Sohl-Dickstein, Jascha and Weiss, Eric A. and Maheswaranathan, Niru and Ganguli, Surya}, 
    title = {Deep unsupervised learning using nonequilibrium thermodynamics}, year = {2015}, publisher = {JMLR.org}, abstract = {A central problem in machine learning involves modeling complex data-sets using highly flexible families of probability distributions in which learning, sampling, inference, and evaluation are still analytically or computationally tractable. Here, we develop an approach that simultaneously achieves both flexibility and tractability. The essential idea, inspired by non-equilibrium statistical physics, is to systematically and slowly destroy structure in a data distribution through an iterative forward diffusion process. We then learn a reverse diffusion process that restores structure in data, yielding a highly flexible and tractable generative model of the data. This approach allows us to rapidly learn, sample from, and evaluate probabilities in deep generative models with thousands of layers or time steps, as well as to compute conditional and posterior probabilities under the learned model. We additionally release an open source reference implementation of the algorithm.}, booktitle = {Proceedings of the 32nd International Conference on International Conference on Machine Learning - Volume 37}, pages = {2256–2265}, numpages = {10}, location = {Lille, France}, series = {ICML'15} 
}

@inproceedings{ddpm_neurips,
 author = {Ho, Jonathan and Jain, Ajay and Abbeel, Pieter},
 booktitle = {Advances in Neural Information Processing Systems},
 editor = {H. Larochelle and M. Ranzato and R. Hadsell and M.F. Balcan and H. Lin},
 pages = {6840--6851},
 publisher = {Curran Associates, Inc.},
 title = {Denoising Diffusion Probabilistic Models},
 url = {https://proceedings.neurips.cc/paper_files/paper/2020/file/4c5bcfec8584af0d967f1ab10179ca4b-Paper.pdf},
 volume = {33},
 year = {2020}
}
@article{song2020denoising,
  title={Denoising Diffusion Implicit Models},
  author={Song, Jiaming and Meng, Chenlin and Ermon, Stefano},
  journal={arXiv:2010.02502},
  year={2020},
  month={October},
  abbr={Preprint},
  url={https://arxiv.org/abs/2010.02502}
}
@inproceedings{
  song2021scorebased,
  title={Score-Based Generative Modeling through Stochastic Differential Equations},
  author={Yang Song and Jascha Sohl-Dickstein and Diederik P Kingma and Abhishek Kumar and Stefano Ermon and Ben Poole},
  booktitle={International Conference on Learning Representations},
  year={2021},
  url={https://openreview.net/forum?id=PxTIG12RRHS}
}

@inproceedings{dm_beats_gans,
 author = {Dhariwal, Prafulla and Nichol, Alexander},
 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 = {8780--8794},
 publisher = {Curran Associates, Inc.},
 title = {Diffusion Models Beat GANs on Image Synthesis},
 url = {https://proceedings.neurips.cc/paper_files/paper/2021/file/49ad23d1ec9fa4bd8d77d02681df5cfa-Paper.pdf},
 volume = {34},
 year = {2021}
}

@inproceedings{xiang2023ddm,
title={{DDM}\${\textasciicircum}2\$: Self-Supervised Diffusion {MRI} Denoising with Generative Diffusion Models},
author={Tiange Xiang and Mahmut Yurt and Ali B Syed and Kawin Setsompop and Akshay Chaudhari},
booktitle={The Eleventh International Conference on Learning Representations },
year={2023},
url={https://openreview.net/forum?id=0vqjc50HfcC}
}

@article{wang2023patch,
  title={Patch Diffusion: Faster and More Data-Efficient Training of Diffusion Models},
  author={Wang, Zhendong and Jiang, Yifan and Zheng, Huangjie and Wang, Peihao and He, Pengcheng and Wang, Zhangyang and Chen, Weizhu and Zhou, Mingyuan},
  journal={arXiv preprint arXiv:2304.12526},
  year={2023}
}

@inproceedings{
moon2022finetuning,
title={Fine-tuning Diffusion Models with Limited Data },
author={Taehong Moon and Moonseok Choi and Gayoung Lee and Jung-Woo Ha and Juho Lee},
booktitle={NeurIPS 2022 Workshop on Score-Based Methods},
year={2022},
url={https://openreview.net/forum?id=0J6afk9DqrR}
}

%% Stylegan1
@INPROCEEDINGS{Karrasstylegan1,  
author={Karras, Tero and Laine, Samuli and Aila, Timo},  booktitle={2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},   
title={A Style-Based Generator Architecture for Generative Adversarial Networks},   year={2019},  volume={},  number={},  pages={4396-4405},  doi={10.1109/CVPR.2019.00453}}

%%v stylegan2
@inproceedings{Karras2019stylegan2,
  title     = {Analyzing and Improving the Image Quality of {StyleGAN}},
  author    = {Tero Karras and Samuli Laine and Miika Aittala and Janne Hellsten and Jaakko Lehtinen and Timo Aila},
  booktitle = {Proc. CVPR},
  year      = {2020}
}
%% stylegan3
@inproceedings{Karras2021stylegan3,
  author = {Tero Karras and Miika Aittala and Samuli Laine and Erik H\"ark\"onen and Janne Hellsten and Jaakko Lehtinen and Timo Aila},
  title = {Alias-Free Generative Adversarial Networks},
  booktitle = {Proc. NeurIPS},
  year = {2021}
}

% conditional GAN
@misc{mirza2014conditional,
      title={Conditional Generative Adversarial Nets}, 
      author={Mehdi Mirza and Simon Osindero},
      year={2014},
      eprint={1411.1784},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}
%% miyato_cgans 
@inproceedings{
miyato2018cgans,
title={c{GAN}s with Projection Discriminator},
author={Takeru Miyato and Masanori Koyama},
booktitle={International Conference on Learning Representations},
year={2018},
url={https://openreview.net/forum?id=ByS1VpgRZ},
}

%% multi-classes conditional art stylegan
@inproceedings{ijcai2022_dobler,
  title     = {Art Creation with Multi-Conditional StyleGANs},
  author    = {Dobler, Konstantin and Hübscher, Florian and Westphal, Jan and Sierra-Múnera, Alejandro and de Melo, Gerard and Krestel, Ralf},
  booktitle = {Proceedings of the Thirty-First International Joint Conference on
               Artificial Intelligence, {IJCAI-22}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  editor    = {Lud De Raedt},
  pages     = {4936--4942},
  year      = {2022},
  month     = {7},
  note      = {AI and Arts},
  doi       = {10.24963/ijcai.2022/684},
  url       = {https://doi.org/10.24963/ijcai.2022/684},
}

%% cyclegan
@INPROCEEDINGS{cyclegan,
  author={Zhu, Jun-Yan and Park, Taesung and Isola, Phillip and Efros, Alexei A.},
  booktitle={2017 IEEE International Conference on Computer Vision (ICCV)}, 
  title={Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks}, 
  year={2017},
  volume={},
  number={},
  pages={2242-2251},
  keywords={Training;Painting;Training data;Semantics;Extraterrestrial measurements;Graphics},
  doi={10.1109/ICCV.2017.244}
}
%% starganv2
@inproceedings{choi2020starganv2,
  title={StarGAN v2: Diverse Image Synthesis for Multiple Domains},
  author={Yunjey Choi and Youngjung Uh and Jaejun Yoo and Jung-Woo Ha},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  year={2020}
}

@inproceedings{multiattr_pizza_gen,
title = "Multi-attribute Pizza Generator: Cross-domain Attribute Control with Conditional StyleGAN",
abstract = "Multi-attribute conditional image generation is a challenging problem in computer vision. We propose Multi-attribute Pizza Generator (MPG), a conditional Generative Neural Network (GAN) framework for synthesizing images from a trichotomy of attributes: content, view-geometry, and implicit visual style. We design MPG by extending the state-of-the-art StyleGAN2, using a new conditioning technique that guides the intermediate feature maps to learn multi-scale multi-attribute entangled representations of controlling attributes. Because of the complex nature of the multi-attribute image generation problem, we regularize the image generation by predicting the explicit conditioning attributes (ingredients and view). To synthesize a pizza image with view attributes outside the range of natural training images, we design a CGI pizza dataset PizzaView using 3D pizza models and employ it to train a view attribute regressor to regularize the generation process, bridging the real and CGI training datasets. To verify the efficacy of MPG, we test it on Pizza10, a carefully annotated multi-ingredient pizza image dataset. MPG can successfully generate photorealistic pizza images with desired ingredients and view attributes, beyond the range of those observed in real-world training data.",
author = "Fangda Han and Guoyao Hao and Ricardo Guerrero and Vladimir Pavlovic",
booktitle = {Proceedings of the British Machine Vision Conference},
note = "Publisher Copyright: {\textcopyright} 2021. The copyright of this document resides with its authors.; 32nd British Machine Vision Conference, BMVC 2021 ; Conference date: 22-11-2021 Through 25-11-2021",
year = "2021",
language = "English (US)",
}

%% Dual Domain face
@article{fu2021dvg,
  title={DVG-face: Dual variational generation for heterogeneous face recognition},
  author={Fu, Chaoyou and Wu, Xiang and Hu, Yibo and Huang, Huaibo and He, Ran},
  journal={IEEE TPAMI},
  year={2021}
}

@article{KANG_structurepreserve,
title = {Structure-preserving image translation for multi-source medical image domain adaptation},
journal = {Pattern Recognition},
volume = {144},
pages = {109840},
year = {2023},
issn = {0031-3203},
doi = {https://doi.org/10.1016/j.patcog.2023.109840},
url = {https://www.sciencedirect.com/science/article/pii/S0031320323005381},
author = {Myeongkyun Kang and Philip Chikontwe and Dongkyu Won and Miguel Luna and Sang Hyun Park},
keywords = {Domain adaptation, Data augmentation, Mutual information, Segmentation, Unpaired image translation},
abstract = {Domain adaptation is an important task for medical image analysis to improve generalization on datasets collected from diverse institutes using different scanners and protocols. For images with visible domain shift, using image translation models is an intuitive and effective way to perform domain adaptation, but the structure of the generated image may often be distorted when large content discrepancies between domains exist; resulting in poor downstream task performance. To address this, we propose a novel image translation model that disentangles structure and texture to only transfer the latter by using mutual information and texture co-occurrence losses. We translate source domain images to the target domain and employ the generated results as augmented samples for domain adaptation segmentation training. We evaluate our method on three public segmentation datasets: MMWHS, Fundus, and Prostate datasets acquired from diverse institutes. Experimental results show that a segmentation model trained using the augmented images from our approach outperforms state-of-the-art domain adaptation, image translation, and domain generalization methods.}
}

%% image harmonization
@article{bashyam2022_harmonization,
  title={Deep generative medical image harmonization for improving cross-site generalization in deep learning predictors},
  author={Bashyam, Vishnu M and Doshi, Jimit and Erus, Guray and Srinivasan, Dhivya and Abdulkadir, Ahmed and Singh, Ashish and Habes, Mohamad and Fan, Yong and Masters, Colin L and Maruff, Paul and others},
  journal={Journal of Magnetic Resonance Imaging},
  volume={55},
  number={3},
  pages={908--916},
  year={2022},
  publisher={Wiley Online Library}
}

@InProceedings{styletransfer_harmonization,
author="Liu, Mengting
and Maiti, Piyush
and Thomopoulos, Sophia
and Zhu, Alyssa
and Chai, Yaqiong
and Kim, Hosung
and Jahanshad, Neda",
editor="de Bruijne, Marleen
and Cattin, Philippe C.
and Cotin, St{\'e}phane
and Padoy, Nicolas
and Speidel, Stefanie
and Zheng, Yefeng
and Essert, Caroline",
title="Style Transfer Using Generative Adversarial Networks for Multi-site MRI Harmonization",
booktitle="Medical Image Computing and Computer Assisted Intervention -- MICCAI 2021",
year="2021",
publisher="Springer International Publishing",
address="Cham",
pages="313--322",
abstract="Large data initiatives and high-powered brain imaging analyses require the pooling of MR images acquired across multiple scanners, often using different protocols. Prospective cross-site harmonization often involves the use of a phantom or traveling subjects. However, as more datasets are becoming publicly available, there is a growing need for retrospective harmonization, pooling data from sites not originally coordinated together. Several retrospective harmonization techniques have shown promise in removing cross-site image variation. However, most unsupervised methods cannot distinguish between image-acquisition based variability and cross-site population variability, so they require that datasets contain subjects or patient groups with similar clinical or demographic information. To overcome this limitation, we consider cross-site MRI image harmonization as a style transfer problem rather than a domain transfer problem. Using a fully unsupervised deep-learning framework based on a generative adversarial network (GAN), we show that MR images can be harmonized by inserting the style information encoded from a reference image directly, without knowing their site/scanner labels a priori. We trained our model using data from five large-scale multi-site datasets with varied demographics. Results demonstrated that our style-encoding model can harmonize MR images, and match intensity profiles, successfully, without relying on traveling subjects. This model also avoids the need to control for clinical, diagnostic, or demographic information. Moreover, we further demonstrated that if we included diverse enough images into the training set, our method successfully harmonized MR images collected from unseen scanners and protocols, suggesting a promising novel tool for ongoing collaborative studies.",
isbn="978-3-030-87199-4"
}

@InProceedings{harmonizing_flow,
author="Beizaee, Farzad
and Desrosiers, Christian
and Lodygensky, Gregory A.
and Dolz, Jose",
editor="Frangi, Alejandro
and de Bruijne, Marleen
and Wassermann, Demian
and Navab, Nassir",
title="Harmonizing Flows: Unsupervised MR Harmonization Based on Normalizing Flows",
booktitle="Information Processing in Medical Imaging",
year="2023",
publisher="Springer Nature Switzerland",
address="Cham",
pages="347--359",
abstract="In this paper, we propose an unsupervised framework based on normalizing flows that harmonizes MR images to mimic the distribution of the source domain. The proposed framework consists of three steps. First, a shallow harmonizer network is trained to recover images of the source domain from their augmented versions. A normalizing flow network is then trained to learn the distribution of the source domain. Finally, at test time, a harmonizer network is modified so that the output images match the source domain's distribution learned by the normalizing flow model. Our unsupervised, source-free and task-independent approach is evaluated on cross-domain brain MRI segmentation using data from four different sites. Results demonstrate its superior performance compared to existing methods. The code is available at https://github.com/farzad-bz/Harmonizing-Flows.",
isbn="978-3-031-34048-2"
}

%% VAN der schaar GAN
@inproceedings{NEURIPS2021_decaf,
 author = {van Breugel, Boris and Kyono, Trent and Berrevoets, Jeroen and van der Schaar, Mihaela},
 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 = {22221--22233},
 publisher = {Curran Associates, Inc.},
 title = {DECAF:  Generating Fair Synthetic Data Using Causally-Aware Generative Networks},
 url = {https://proceedings.neurips.cc/paper/2021/file/ba9fab001f67381e56e410575874d967-Paper.pdf},
 volume = {34},
 year = {2021}
}

%% LPIPS
@inproceedings{zhang2018perceptual,
  title={The Unreasonable Effectiveness of Deep Features as a Perceptual Metric},
  author={Zhang, Richard and Isola, Phillip and Efros, Alexei A and Shechtman, Eli and Wang, Oliver},
  booktitle={CVPR},
  year={2018}
}

### Causal Inference
%% graph dag
@article{cdag_graph,
author = {Thulasiraman, K. and Swamy, Mallappa},
year = {2011},
month = {02},
pages = {i-xvi},
title = {Graphs: Theory and Algorithms},
doi = {10.1002/9781118033104.fmatter}
}

%% causal counterfactual
@article{structural_counterfactuals,
author = {Pearl, Judea},
title = {Structural Counterfactuals: A Brief Introduction},
journal = {Cognitive Science},
volume = {37},
number = {6},
pages = {977-985},
keywords = {Causal reasoning, Counterfactuals, Structural models},
doi = {https://doi.org/10.1111/cogs.12065},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/cogs.12065},
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1111/cogs.12065},
abstract = {Abstract Recent advances in causal reasoning have given rise to a computational model that emulates the process by which humans generate, evaluate, and distinguish counterfactual sentences. Contrasted with the “possible worlds” account of counterfactuals, this “structural” model enjoys the advantages of representational economy, algorithmic simplicity, and conceptual clarity. This introduction traces the emergence of the structural model and gives a panoramic view of several applications where counterfactual reasoning has benefited problem areas in the empirical sciences.},
year = {2013}
}
%bayesian
@inproceedings{bayesian_gan_yunus, author = {Saatchi, Yunus and Wilson, Andrew Gordon}, title = {Bayesian GAN}, year = {2017}, isbn = {9781510860964}, publisher = {Curran Associates Inc.}, address = {Red Hook, NY, USA}, abstract = {Generative adversarial networks (GANs) can implicitly learn rich distributions over images, audio, and data which are hard to model with an explicit likelihood. We present a practical Bayesian formulation for unsupervised and semi-supervised learning with GANs. Within this framework, we use stochastic gradient Hamiltonian Monte Carlo to marginalize the weights of the generator and discriminator networks. The resulting approach is straightforward and obtains good performance without any standard interventions such as label smoothing or mini-batch discrimination. By exploring an expressive posterior over the parameters of the generator, the Bayesian GAN avoids mode-collapse, produces interpretable and diverse candidate samples, and provides state-of-the-art quantitative results for semi-supervised learning on benchmarks including SVHN, CelebA, and CIFAR-10, outperforming DCGAN, Wasserstein GANs, and DCGAN ensembles.}, booktitle = {Proceedings of the 31st International Conference on Neural Information Processing Systems}, pages = {3625–3634}, numpages = {10}, location = {Long Beach, California, USA}, series = {NIPS'17} }
%% GAN review in medical domains
@article{reviewgans_Kazeminia2020,
abstract = {Generative adversarial networks (GANs) and their extensions have carved open many exciting ways to tackle well known and challenging medical image analysis problems such as medical image de-noising, reconstruction, segmentation, data simulation, detection or classification. Furthermore, their ability to synthesize images at unprecedented levels of realism also gives hope that the chronic scarcity of labeled data in the medical field can be resolved with the help of these generative models. In this review paper, a broad overview of recent literature on GANs for medical applications is given, the shortcomings and opportunities of the proposed methods are thoroughly discussed, and potential future work is elaborated. We review the most relevant papers published until the submission date. For quick access, essential details such as the underlying method, datasets, and performance are tabulated. An interactive visualization that categorizes all papers to keep the review alive is available at http://livingreview.in.tum.de/GANs{\_}for{\_}Medical{\_}Applications/.},
author = {Kazeminia, Salome and Baur, Christoph and Kuijper, Arjan and van Ginneken, Bram and Navab, Nassir and Albarqouni, Shadi and Mukhopadhyay, Anirban},
doi = {10.1016/j.artmed.2020.101938},
issn = {18732860},
journal = {Artificial Intelligence in Medicine},
keywords = {Deep learning,Generative adversarial networks,Medical imaging,Survey},
mendeley-groups = {PhD/Deep Learning},
pages = {101938},
publisher = {Elsevier B.V.},
title = {{GANs for medical image analysis}},
url = {https://doi.org/10.1016/j.artmed.2020.101938},
volume = {109},
year = {2020}
}

% AI Fairly
@article{Bak2022,
author = {Bak, Marieke and Madai, Vince Istvan and Fritzsche, Marie-christine and Mayrhofer, Michaela Th},
doi = {10.3389/fgene.2022.929453},
file = {:C$\backslash$:/Users/jana.fehr/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Bak et al. - 2022 - You Can't Have AI Both Ways Balancing Health Data Privacy and Access Fairly.pdf:pdf},
journal = {Frontiers in Genetics},
keywords = {Bak2022,arti fi cial intelligence,data access,data privacy,digital health,ethics,fairness,resource allocation},
mendeley-groups = {PhD/Digital Health},
mendeley-tags = {Bak2022},
number = {June},
pages = {1--7},
title = {{You Can't Have AI Both Ways : Balancing Health Data Privacy and Access Fairly}},
volume = {13},
year = {2022}
}

% using cnn, gan downstream task classification
@article{Frid-Adar2018_gan,
abstract = {Deep learning methods, and in particular convolutional neural networks (CNNs), have led to an enormous breakthrough in a wide range of computer vision tasks, primarily by using large-scale annotated datasets. However, obtaining such datasets in the medical domain remains a challenge. In this paper, we present methods for generating synthetic medical images using recently presented deep learning Generative Adversarial Networks (GANs). Furthermore, we show that generated medical images can be used for synthetic data augmentation, and improve the performance of CNN for medical image classification. Our novel method is demonstrated on a limited dataset of computed tomography (CT) images of 182 liver lesions (53 cysts, 64 metastases and 65 hemangiomas). We first exploit GAN architectures for synthesizing high quality liver lesion ROIs. Then we present a novel scheme for liver lesion classification using CNN. Finally, we train the CNN using classic data augmentation and our synthetic data augmentation and compare performance. In addition, we explore the quality of our synthesized examples using visualization and expert assessment. The classification performance using only classic data augmentation yielded 78.6{\%} sensitivity and 88.4{\%} specificity. By adding the synthetic data augmentation the results increased to 85.7{\%} sensitivity and 92.4{\%} specificity. We believe that this approach to synthetic data augmentation can generalize to other medical classification applications and thus support radiologists' efforts to improve diagnosis.},
archivePrefix = {arXiv},
arxivId = {1803.01229},
author = {Frid-Adar, Maayan and Diamant, Idit and Klang, Eyal and Amitai, Michal and Goldberger, Jacob and Greenspan, Hayit},
doi = {10.1016/J.NEUCOM.2018.09.013},
eprint = {1803.01229},
issn = {0925-2312},
journal = {Neurocomputing},
keywords = {Convolutional neural networks,Data augmentation,Deep learning,Generative adversarial network,Image synthesis,Lesion classification,Liver lesions},
mendeley-groups = {Brain MRI applications/Generating brain MRIs},
month = {dec},
pages = {321--331},
publisher = {Elsevier},
title = {{GAN-based synthetic medical image augmentation for increased CNN performance in liver lesion classification}},
volume = {321},
year = {2018}
}

%% medical gan 
@article{Han2018_ganMR_imaging,
abstract = {In medical imaging, it remains a challenging and valuable goal how to generate realistic medical images completely different from the original ones; the obtained synthetic images would improve diagnostic reliability, allowing for data augmentation in computer-assisted diagnosis as well as physician training. In this paper, we focus on generating synthetic multi-sequence brain Magnetic Resonance (MR) images using Generative Adversarial Networks (GANs). This involves difficulties mainly due to low contrast MR images, strong consistency in brain anatomy, and intra-sequence variability. Our novel realistic medical image generation approach shows that GANs can generate 128 $\chi$ 128 brain MR images avoiding artifacts. In our preliminary validation, even an expert physician was unable to accurately distinguish the synthetic images from the real samples in the Visual Turing Test.},
author = {Han, Changhee and Hayashi, Hideaki and Rundo, Leonardo and Araki, Ryosuke and Shimoda, Wataru and Muramatsu, Shinichi and Furukawa, Yujiro and Mauri, Giancarlo and Nakayama, Hideki},
doi = {10.1109/ISBI.2018.8363678},
isbn = {9781538636367},
issn = {19458452},
journal = {Proceedings - International Symposium on Biomedical Imaging},
keywords = {Brain MRI,Data Augmentation,Generative Adversarial Networks,Physician Training,Synthetic Medical Image Generation,Visual Turing Test},
mendeley-groups = {Brain MRI applications/Generating brain MRIs},
month = {may},
pages = {734--738},
publisher = {IEEE Computer Society},
title = {{GAN-based synthetic brain MR image generation}},
volume = {2018-April},
year = {2018}
}

%% 3d stylegan
@article{3Dstylegan,
abstract = {Image synthesis via Generative Adversarial Networks (GANs) of three-dimensional (3D) medical images has great potential that can be extended to many medical applications, such as, image enhancement and disease progression modeling. However, current GAN technologies for 3D medical image synthesis need to be significantly improved to be readily adapted to real-world medical problems. In this paper, we extend the state-of-the-art StyleGAN2 model, which natively works with two-dimensional images, to enable 3D image synthesis. In addition to the image synthesis, we investigate the controllability and interpretability of the 3D-StyleGAN via style vectors inherited form the original StyleGAN2 that are highly suitable for medical applications: (i) the latent space projection and reconstruction of unseen real images, and (ii) style mixing. We demonstrate the 3D-StyleGAN's performance and feasibility with ∼12,000 three-dimensional full brain MR T1 images, although it can be applied to any 3D volumetric images. Furthermore, we explore different configurations of hyperparameters to investigate potential improvement of the image synthesis with larger networks. The codes and pre-trained networks are available online: https://github.com/sh4174/3DStyleGAN.},
annote = {https://github.com/sh4174/3DStyleGAN

pretrained models available!},
archivePrefix = {arXiv},
arxivId = {2107.09700v1},
author = {Hong, Sungmin and Marinescu, Razvan and Dalca, Adrian V and Bonkhoff, Anna K and Bretzner, Martin and Rost, Natalia S and Golland, Polina},
eprint = {2107.09700v1},
file = {:C$\backslash$:/Users/jana.fehr/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Hong et al. - 2021 - 3D-StyleGAN A Style-Based Generative Adversarial Network for Generative Modeling of Three-Dimensional Medical Image.pdf:pdf},
mendeley-groups = {Brain MRI applications/Generating brain MRIs},
title = {{3D-StyleGAN: A Style-Based Generative Adversarial Network for Generative Modeling of Three-Dimensional Medical Images}},
url = {https://github.com/sh4174/3DStyleGAN},
year = {2021}
}

%% Retinal GANs
@article{ZhaoLC17,
  author       = {He Zhao and
                  Huiqi Li and
                  Li Cheng},
  title        = {Synthesizing Filamentary Structured Images with GANs},
  journal      = {CoRR},
  volume       = {abs/1706.02185},
  year         = {2017},
  url          = {http://arxiv.org/abs/1706.02185},
  eprinttype    = {arXiv},
  eprint       = {1706.02185},
  timestamp    = {Tue, 23 Nov 2021 15:18:14 +0100},
  biburl       = {https://dblp.org/rec/journals/corr/ZhaoLC17.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@article{CostaGMANMC17,
  author       = {Pedro Costa and
                  Adrian Galdran and
                  Maria In{\^{e}}s Meyer and
                  Michael David Abr{\`{a}}moff and
                  Meindert Niemeijer and
                  Ana Maria Mendon{\c{c}}a and
                  Aur{\'{e}}lio Campilho},
  title        = {Towards Adversarial Retinal Image Synthesis},
  journal      = {CoRR},
  volume       = {abs/1701.08974},
  year         = {2017},
  url          = {http://arxiv.org/abs/1701.08974},
  eprinttype    = {arXiv},
  eprint       = {1701.08974},
  timestamp    = {Tue, 17 Sep 2019 14:15:29 +0200},
  biburl       = {https://dblp.org/rec/journals/corr/CostaGMANMC17.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@ARTICLE{End2EndAdversarial_retinal,
  author={Costa, Pedro and Galdran, Adrian and Meyer, Maria Ines and Niemeijer, Meindert and Abràmoff, Michael and Mendonça, Ana Maria and Campilho, Aurélio},
  journal={IEEE Transactions on Medical Imaging}, 
  title={End-to-End Adversarial Retinal Image Synthesis}, 
  year={2018},
  volume={37},
  number={3},
  pages={781-791},
  doi={10.1109/TMI.2017.2759102}
}

@inproceedings{Reinhold2021,
abstract = {Precision medicine involves answering counterfactual questions such as "Would this patient respond better to treatment A or treatment B?" These types of questions are causal in nature and require the tools of causal inference to be answered, e.g., with a structural causal model (SCM). In this work, we develop an SCM that models the interaction between demographic information, disease covariates, and magnetic resonance (MR) images of the brain for people with multiple sclerosis. Inference in the SCM generates counterfactual images that show what an MR image of the brain would look like if demographic or disease covariates are changed. These images can be used for modeling disease progression or used for image processing tasks where controlling for confounders is necessary.},
annote = {A continuation of Pawlowski et al. - they extend the model beyond just healthy subjects, increase the sample resolution from 64 to 128/224, and use a hierarchical latent space , divisible into high-level binary features and low-level noise.

They use a lot of tricks to train the model (which kind of contradicts the motivation they state for using VAEs, as opposed to GANs, because they are easier to train).

I am not convinced for why such a complicated approach is needed - a lot of the choices seem arbitrary for me (some ablation tests would be neat). Especially the highly-dimensional low-level latent space, which is basically an image itself.

The authors themselves note that "(...)our model created poor counterfactual images for images outside the training set."
This is not surprising given their small N=70 and huge model.
This could make it unsuitable for our work (unless the lack of generalization can be simply mitigated with larger sample size).},
archivePrefix = {arXiv},
arxivId = {2103.03158v3},
author = {Reinhold, Jacob C and Carass, Aaron and Prince, Jerry L},
booktitle = {Medical Image Computing and Computer Assisted Intervention – MICCAI 2021},
eprint = {2103.03158v3},
file = {:C$\backslash$:/Users/jana.fehr/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Reinhold, Carass, Prince - 2021 - A Structural Causal Model for MR Images of Multiple Sclerosis.pdf:pdf},
keywords = {Causal inference {\textperiodcentered} multiple sclerosis {\textperiodcentered} MRI},
mendeley-groups = {Causal Generative Models,Brain MRI applications/Generating brain MRIs},
title = {{A Structural Causal Model for MR Images of Multiple Sclerosis}},
year = {2021}
}

%% sagan
@InProceedings{pmlr-v97-zhang19d_sagan,
  title = 	 {Self-Attention Generative Adversarial Networks},
  author =       {Zhang, Han and Goodfellow, Ian and Metaxas, Dimitris and Odena, Augustus},
  booktitle = 	 {Proceedings of the 36th International Conference on Machine Learning},
  pages = 	 {7354--7363},
  year = 	 {2019},
  editor = 	 {Chaudhuri, Kamalika and Salakhutdinov, Ruslan},
  volume = 	 {97},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {09--15 Jun},
  publisher =    {PMLR},
  pdf = 	 {http://proceedings.mlr.press/v97/zhang19d/zhang19d.pdf},
  url = 	 {https://proceedings.mlr.press/v97/zhang19d.html},
  abstract = 	 {In this paper, we propose the Self-Attention Generative Adversarial Network (SAGAN) which allows attention-driven, long-range dependency modeling for image generation tasks. Traditional convolutional GANs generate high-resolution details as a function of only spatially local points in lower-resolution feature maps. In SAGAN, details can be generated using cues from all feature locations. Moreover, the discriminator can check that highly detailed features in distant portions of the image are consistent with each other. Furthermore, recent work has shown that generator conditioning affects GAN performance. Leveraging this insight, we apply spectral normalization to the GAN generator and find that this improves training dynamics. The proposed SAGAN performs better than prior work, boosting the best published Inception score from 36.8 to 52.52 and reducing Fréchet Inception distance from 27.62 to 18.65 on the challenging ImageNet dataset. Visualization of the attention layers shows that the generator leverages neighborhoods that correspond to object shapes rather than local regions of fixed shape.}
}

@inproceedings{Pawlowski2020,
abstract = {We formulate a general framework for building structural causal models (SCMs) with deep learning components. The proposed approach employs normalising flows and variational inference to enable tractable inference of exogenous noise variables-a crucial step for counterfactual inference that is missing from existing deep causal learning methods. Our framework is validated on a synthetic dataset built on MNIST as well as on a real-world medical dataset of brain MRI scans. Our experimental results indicate that we can successfully train deep SCMs that are capable of all three levels of Pearl's ladder of causation: association, intervention, and counterfactuals, giving rise to a powerful new approach for answering causal questions in imaging applications and beyond. The code for all our experiments is available at https://github.com/biomedia-mira/deepscm.},
address = {Vancouver},
annote = {Experiments performed on a synthetic modification of MNIST with a known SEM, and on 3D UKBB Brain MRIs (hence probably interesting for us to compare against). They specified a simple DAG for some brain variables (age, sex, brain/ventricle volume, image).

They employ a combination of flows for learning the latent distr. and VAEs for image generation.

First they prove that a fully causal model performs empirically better on the MNIST data, where they have direct access to all the variables. For UKBB they evaluate by showing some interventions on real data, which look plausible indeed (e.g., increased volumes). 

Random samples however look terrible - potential room for improvement for us? They also have code on github, the repo looks nice at first sight},
archivePrefix = {arXiv},
arxivId = {2006.06485v2},
author = {Pawlowski, Nick and Castro, Daniel C and Glocker, Ben},
booktitle = {Advances in Neural Information Processing Systems 34},
eprint = {2006.06485v2},
file = {:C$\backslash$:/Users/jana.fehr/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Pawlowski, Castro, Glocker - 2020 - Deep Structural Causal Models for Tractable Counterfactual Inference(3).pdf:pdf},
mendeley-groups = {Causal Generative Models,Brain MRI applications/Generating brain MRIs},
title = {{Deep Structural Causal Models for Tractable Counterfactual Inference}},
url = {https://github.com/biomedia-mira/deepscm},
year = {2020}
}

%% cond 3d
@InProceedings{Jung2021_cond3D,
author="Jung, Euijin
and Luna, Miguel
and Park, Sang Hyun",
editor="de Bruijne, Marleen
and Cattin, Philippe C.
and Cotin, St{\'e}phane
and Padoy, Nicolas
and Speidel, Stefanie
and Zheng, Yefeng
and Essert, Caroline",
title="Conditional GAN with an Attention-Based Generator and a 3D Discriminator for 3D Medical Image Generation",
booktitle="Medical Image Computing and Computer Assisted Intervention -- MICCAI 2021",
year="2021",
publisher="Springer International Publishing",
address="Cham",
pages="318--328",
abstract="Conditional Generative Adversarial Networks (cGANs) are a set of methods able to synthesize images that match a given condition. However, existing models designed for natural images are impractical to generate high-quality 3D medical images due to enormous computation. To address this issue, most cGAN models used in the medical field process either 2D slices or small 3D crops and join them together in subsequent steps to reconstruct the full-size 3D image. However, these approaches often cause spatial inconsistencies in adjacent slices or crops, and the changes specified by the target condition may not consider the 3D image as a whole. To address these problems, we propose a novel cGAN that can synthesize high-quality 3D MR images at different stages of the Alzheimer's disease (AD). First, our method generates a sequence of 2D slices using an attention-based 2D generator with a disease condition for efficient transformations depending on brain regions. Then, consistency in 3D space is enforced by the use of a set of 2D and 3D discriminators. Moreover, we propose an adaptive identity loss based on the attention scores to properly transform features relevant to the target condition. Our experiments show that the proposed method can generate smooth and realistic 3D images at different stages of AD, and the image change with respect to the condition is better than the images generated by existing GAN-based methods.",
isbn="978-3-030-87231-1"
}

%%% Multi-Sources
@inproceedings{deligan, 
    title={DeLiGAN: Generative Adversarial Networks for Diverse and Limited Data}, 
    ISSN={1063-6919}, 
    DOI={10.1109/CVPR.2017.525}, 
    abstractNote={A class of recent approaches for generating images, called Generative Adversarial Networks (GAN), have been used to generate impressively realistic images of objects, bedrooms, handwritten digits and a variety of other image modalities. However, typical GAN-based approaches require large amounts of training data to capture the diversity across the image modality. In this paper, we propose DeLiGAN - a novel GAN-based architecture for diverse and limited training data scenarios. In our approach, we reparameterize the latent generative space as a mixture model and learn the mixture models parameters along with those of GAN. This seemingly simple modification to the GAN framework is surprisingly effective and results in models which enable diversity in generated samples although trained with limited data. In our work, we show that DeLiGAN can generate images of handwritten digits, objects and hand-drawn sketches, all using limited amounts of data. To quantitatively characterize intra-class diversity of generated samples, we also introduce a modified version of inception-score, a measure which has been found to correlate well with human assessment of generated samples.}, booktitle={2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, author={Gurumurthy, Swaminathan and Sarvadevabhatla, Ravi Kiran and Babu, R. Venkatesh}, year={2017}, month={Jul}, pages={4941–4949} 
}

@article{karras2020training,
  title={Training generative adversarial networks with limited data},
  author={Karras, Tero and Aittala, Miika and Hellsten, Janne and Laine, Samuli and Lehtinen, Jaakko and Aila, Timo},
  journal={Advances in neural information processing systems},
  volume={33},
  pages={12104--12114},
  year={2020}
}

@inproceedings{jointgan, 
    title={JointGAN: Multi-Domain Joint Distribution Learning with Generative Adversarial Nets}, ISSN={2640-3498}, url={https://proceedings.mlr.press/v80/pu18a.html}, 
    abstractNote={A new generative adversarial network is developed for joint distribution matching.Distinct from most existing approaches, that only learn conditional distributions, the proposed model aims to learn a joint distribution of multiple random variables (domains). This is achieved by learning to sample from conditional distributions between the domains, while simultaneously learning to sample from the marginals of each individual domain.The proposed framework consists of multiple generators and a single softmax-based critic, all jointly trained via adversarial learning.From a simple noise source, the proposed framework allows synthesis of draws from the marginals, conditional draws given observations from a subset of random variables, or complete draws from the full joint distribution. Most examples considered are for joint analysis of two domains, with examples for three domains also presented.}, booktitle={Proceedings of the 35th International Conference on Machine Learning}, publisher={PMLR}, author={Pu, Yunchen and Dai, Shuyang and Gan, Zhe and Wang, Weiyao and Wang, Guoyin and Zhang, Yizhe and Henao, Ricardo and Duke, Lawrence Carin}, year={2018}, month={Jul}, pages={4151–4160}, language={en} 
}

@article{Dumoulin_Belghazi_ali_2017, title={Adversarially Learned Inference}, url={http://arxiv.org/abs/1606.00704}, DOI={10.48550/arXiv.1606.00704}, abstractNote={We introduce the adversarially learned inference (ALI) model, which jointly learns a generation network and an inference network using an adversarial process. The generation network maps samples from stochastic latent variables to the data space while the inference network maps training examples in data space to the space of latent variables. An adversarial game is cast between these two networks and a discriminative network is trained to distinguish between joint latent/data-space samples from the generative network and joint samples from the inference network. We illustrate the ability of the model to learn mutually coherent inference and generation networks through the inspections of model samples and reconstructions and confirm the usefulness of the learned representations by obtaining a performance competitive with state-of-the-art on the semi-supervised SVHN and CIFAR10 tasks.}, note={arXiv:1606.00704 [cs, stat]}, number={arXiv:1606.00704}, publisher={arXiv}, author={Dumoulin, Vincent and Belghazi, Ishmael and Poole, Ben and Mastropietro, Olivier and Lamb, Alex and Arjovsky, Martin and Courville, Aaron}, year={2017}, month={Feb} }


%% state of the art gans
@ARTICLE{stateoftheart_gan,
  author={Wang, Lei and Chen, Wei and Yang, Wenjia and Bi, Fangming and Yu, Fei Richard},
  journal={IEEE Access}, 
  title={A State-of-the-Art Review on Image Synthesis With Generative Adversarial Networks}, 
  year={2020},
  volume={8},
  number={},
  pages={63514-63537},
  doi={10.1109/ACCESS.2020.2982224}
}
% Vanilla GAN
@inproceedings{goodfellow2014gan, 
  title={Generative adversarial nets}, 
  author={Goodfellow, Ian and Pouget-Abadie, Jean and Mirza, Mehdi and Xu, Bing and Warde-Farley, David and Ozair, Sherjil and Courville, Aaron and Bengio, Yoshua}, 
  booktitle={Advances in neural information processing systems}, 
  pages={2672--2680}, 
  year={2014} 
}
%% biggan 
@inproceedings{
brock2018large,
title={Large Scale {GAN} Training for High Fidelity Natural Image Synthesis},
author={Andrew Brock and Jeff Donahue and Karen Simonyan},
booktitle={International Conference on Learning Representations},
year={2019},
url={https://openreview.net/forum?id=B1xsqj09Fm},
}
%% progressive GAN
@inproceedings{
karras2018progressive,
title={Progressive Growing of {GAN}s for Improved Quality, Stability, and Variation},
author={Tero Karras and Timo Aila and Samuli Laine and Jaakko Lehtinen},
booktitle={International Conference on Learning Representations},
year={2018},
url={https://openreview.net/forum?id=Hk99zCeAb},
}
%% conditional BatchNorm
@inproceedings{
conditionalBatchNorm,
title={A Learned Representation For Artistic Style},
author={Vincent Dumoulin and Jonathon Shlens and Manjunath Kudlur},
booktitle={International Conference on Learning Representations},
year={2017},
url={https://openreview.net/forum?id=BJO-BuT1g}
}

%% ccgan continuous
@inproceedings{
continuous_ding2021ccgan,
title={Cc{\{}GAN{\}}: Continuous Conditional Generative Adversarial Networks for Image Generation},
author={Xin Ding and Yongwei Wang and Zuheng Xu and William J Welch and Z. Jane Wang},
booktitle={International Conference on Learning Representations},
year={2021},
url={https://openreview.net/forum?id=PrzjugOsDeE}
}

%% Causal GAN
@inproceedings{
kocaoglu2018causalgan,
title={Causal{GAN}: Learning Causal Implicit Generative Models with Adversarial Training},
author={Murat Kocaoglu and Christopher Snyder and Alexandros G. Dimakis and Sriram Vishwanath},
booktitle={International Conference on Learning Representations},
year={2018},
url={https://openreview.net/forum?id=BJE-4xW0W},
}

%%% Diastolic BP
@article{predict_cardiovascular_retinal_poplin,
title={Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning}, 
volume={2}, 
url={https://storage.googleapis.com/pub-tools-public-publication-data/pdf/a79654ba4743b72dda1d64045518be1bc127a9dd.pdf}, 
DOI={https://doi.org/10.1038/s41551-018-0195-0}, number={3}, journal={Nature Biomedical Engineering}, 
author={Poplin, Ryan and Varadarajan, Avinash V. and Blumer, Katy and Liu, Yun and McConnell, Michael V. and Corrado, Greg S. and Peng, Lily and Webster, Dale R.}, 
year={2018}, month={Feb}, pages={158–164} 
}
%% hypertensive retinopathy
@article{hypertensive_retinopathy, 
title={Hypertensive eye disease}, volume={8}, url={https://www.nature.com/articles/s41572-022-00342-0}, 
DOI={https://doi.org/10.1038/s41572-022-00342-0}, number={1}, journal={Nature Reviews Disease Primers}, author={Cheung, Carol Y. and Biousse, Valérie and Keane, Pearse A. and Schiffrin, Ernesto L. and Wong, Tien Y.}, year={2022}, month={Mar}, pages={1–18} 
}

% FID score 
@inproceedings{fid_score, author = {Heusel, Martin and Ramsauer, Hubert and Unterthiner, Thomas and Nessler, Bernhard and Hochreiter, Sepp}, title = {GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium}, year = {2017}, isbn = {9781510860964}, publisher = {Curran Associates Inc.}, address = {Red Hook, NY, USA}, abstract = {Generative Adversarial Networks (GANs) excel at creating realistic images with complex models for which maximum likelihood is infeasible. However, the convergence of GAN training has still not been proved. We propose a two time-scale update rule (TTUR) for training GANs with stochastic gradient descent on arbitrary GAN loss functions. TTUR has an individual learning rate for both the discriminator and the generator. Using the theory of stochastic approximation, we prove that the TTUR converges under mild assumptions to a stationary local Nash equilibrium. The convergence carries over to the popular Adam optimization, for which we prove that it follows the dynamics of a heavy ball with friction and thus prefers flat minima in the objective landscape. For the evaluation of the performance of GANs at image generation, we introduce the 'Fr\'{e}chet Inception Distance" (FID) which captures the similarity of generated images to real ones better than the Inception Score. In experiments, TTUR improves learning for DCGANs and Improved Wasserstein GANs (WGAN-GP) outperforming conventional GAN training on CelebA, CIFAR-10, SVHN, LSUN Bedrooms, and the One Billion Word Benchmark.}, booktitle = {Proceedings of the 31st International Conference on Neural Information Processing Systems}, pages = {6629–6640}, numpages = {12}, location = {Long Beach, California, USA}, series = {NIPS'17} }

% Inception score
@inproceedings{inception_score, author = {Salimans, Tim and Goodfellow, Ian and Zaremba, Wojciech and Cheung, Vicki and Radford, Alec and Chen, Xi}, title = {Improved Techniques for Training GANs}, year = {2016}, isbn = {9781510838819}, publisher = {Curran Associates Inc.}, address = {Red Hook, NY, USA}, abstract = {We present a variety of new architectural features and training procedures that we apply to the generative adversarial networks (GANs) framework. Using our new techniques, we achieve state-of-the-art results in semi-supervised classification on MNIST, CIFAR-10 and SVHN. The generated images are of high quality as confirmed by a visual Turing test: our model generates MNIST samples that humans cannot distinguish from real data, and CIFAR-10 samples that yield a human error rate of 21.3%. We also present ImageNet samples with unprecedented resolution and show that our methods enable the model to learn recognizable features of ImageNet classes.}, booktitle = {Proceedings of the 30th International Conference on Neural Information Processing Systems}, pages = {2234–2242}, numpages = {9}, location = {Barcelona, Spain}, series = {NIPS'16} }‌

% Kernel MMD
@inproceedings{kernel_mmd, author = {Gretton, Arthur and Borgwardt, Karsten M. and Rasch, Malte and Sch\"{o}lkopf, Bernhard and Smola, Alexander J.}, title = {A Kernel Method for the Two-Sample-Problem}, year = {2006}, publisher = {MIT Press}, address = {Cambridge, MA, USA}, abstract = {We propose two statistical tests to determine if two samples are from different distributions. Our test statistic is in both cases the distance between the means of the two samples mapped into a reproducing kernel Hilbert space (RKHS). The first test is based on a large deviation bound for the test statistic, while the second is based on the asymptotic distribution of this statistic. The test statistic can be computed in O(m2) time. We apply our approach to a variety of problems, including attribute matching for databases using the Hungarian marriage method, where our test performs strongly. We also demonstrate excellent performance when comparing distributions over graphs, for which no alternative tests currently exist.}, booktitle = {Proceedings of the 19th International Conference on Neural Information Processing Systems}, pages = {513–520}, numpages = {8}, location = {Canada}, series = {NIPS'06} 
}
%% Precision and recall
@inbook{precision_recall_gan, author = {Kynk\"{a}\"{a}nniemi, Tuomas and Karras, Tero and Laine, Samuli and Lehtinen, Jaakko and Aila, Timo}, title = {Improved Precision and Recall Metric for Assessing Generative Models}, year = {2019}, publisher = {Curran Associates Inc.}, address = {Red Hook, NY, USA}, abstract = {The ability to automatically estimate the quality and coverage of the samples produced by a generative model is a vital requirement for driving algorithm research. We present an evaluation metric that can separately and reliably measure both of these aspects in image generation tasks by forming explicit, non-parametric representations of the manifolds of real and generated data. We demonstrate the effectiveness of our metric in StyleGAN and BigGAN by providing several illustrative examples where existing metrics yield uninformative or contradictory results. Furthermore, we analyze multiple design variants of StyleGAN to better understand the relationships between the model architecture, training methods, and the properties of the resulting sample distribution. In the process, we identify new variants that improve the state-of-the-art. We also perform the first principled analysis of truncation methods and identify an improved method. Finally, we extend our metric to estimate the perceptual quality of individual samples, and use this to study latent space interpolations.}, booktitle = {Proceedings of the 33rd International Conference on Neural Information Processing Systems}, articleno = {353}, numpages = {10} }

%% ENlarge ventricle
@article{age_ventricular,
author = {Kwon, Yong Hyun and Jang, Sung and Yeo, Sang Seok},
year = {2014},
month = {05},
pages = {986-9},
title = {Age-related changes of lateral ventricular width and periventricular white matter in the human brain: A diffusion tensor imaging study},
volume = {9},
journal = {Neural regeneration research},
doi = {10.4103/1673-5374.133152}
}

@article{brain_enlarged_ventricle,
    author = {Nestor, Sean M. and Rupsingh, Raul and Borrie, Michael and Smith, Matthew and Accomazzi, Vittorio and Wells, Jennie L. and Fogarty, Jennifer and Bartha, Robert and the Alzheimer's Disease Neuroimaging Initiative},
    title = "{Ventricular enlargement as a possible measure of Alzheimer's disease progression validated using the Alzheimer's disease neuroimaging initiative database}",
    journal = {Brain},
    volume = {131},
    number = {9},
    pages = {2443-2454},
    year = {2008},
    month = {07},
    abstract = "{Ventricular enlargement may be an objective and sensitive measure of neuropathological change associated with mild cognitive impairment (MCI) and Alzheimer's disease (AD), suitable to assess disease progression for multi-centre studies. This study compared (i) ventricular enlargement after six months in subjects with MCI, AD and normal elderly controls (NEC) in a multi-centre study, (ii) volumetric and cognitive changes between Apolipoprotein E genotypes, (iii) ventricular enlargement in subjects who progressed from MCI to AD, and (iv) sample sizes for multi-centre MCI and AD studies based on measures of ventricular enlargement. Three dimensional T1-weighted MRI and cognitive measures were acquired from 504 subjects (NEC n = 152, MCI n = 247 and AD n = 105) participating in the multi-centre Alzheimer's Disease Neuroimaging Initiative. Cerebral ventricular volume was quantified at baseline and after six months using semi-automated software. For the primary analysis of ventricle and neurocognitive measures, between group differences were evaluated using an analysis of covariance, and repeated measures t-tests were used for within group comparisons. For secondary analyses, all groups were dichotomized for Apolipoprotein E genotype based on the presence of an ε4 polymorphism. In addition, the MCI group was dichotomized into those individuals who progressed to a clinical diagnosis of AD, and those subjects that remained stable with MCI after six months. Group differences on neurocognitive and ventricle measures were evaluated by independent t-tests. General sample size calculations were computed for all groups derived from ventricle measurements and neurocognitive scores. The AD group had greater ventricular enlargement compared to both subjects with MCI (P = 0.0004) and NEC (P\\&lt; 0.0001), and subjects with MCI had a greater rate of ventricular enlargement compared to NEC (P = 0.0001). MCI subjects that progressed to clinical AD after six months had greater ventricular enlargement than stable MCI subjects (P = 0.0270). Ventricular enlargement was different between Apolipoprotein E genotypes within the AD group (P = 0.010). The number of subjects required to demonstrate a 20\\% change in ventricular enlargement was substantially lower than that required to demonstrate a 20\\% change in cognitive scores. Ventricular enlargement represents a feasible short-term marker of disease progression in subjects with MCI and subjects with AD for multi-centre studies.}",
    issn = {0006-8950},
    doi = {10.1093/brain/awn146},
    url = {https://doi.org/10.1093/brain/awn146},
    eprint = {https://academic.oup.com/brain/article-pdf/131/9/2443/792447/awn146.pdf},
}


%% grey matter 
@article{age_related_change_greymatter,
title = {Age-related changes in grey and white matter structure throughout adulthood},
journal = {NeuroImage},
volume = {51},
number = {3},
pages = {943-951},
year = {2010},
issn = {1053-8119},
doi = {https://doi.org/10.1016/j.neuroimage.2010.03.004},
url = {https://www.sciencedirect.com/science/article/pii/S1053811910002740},
author = {Antonio Giorgio and Luca Santelli and Valentina Tomassini and Rose Bosnell and Steve Smith and Nicola {De Stefano} and Heidi Johansen-Berg},
abstract = {Normal ageing is associated with gradual brain atrophy. Determining spatial and temporal patterns of change can help shed light on underlying mechanisms. Neuroimaging provides various measures of brain structure that can be used to assess such age-related change but studies to date have typically considered single imaging measures. Although there is consensus on the notion that brain structure deteriorates with age, evidence on the precise time course and spatial distribution of changes is mixed. We assessed grey matter (GM) and white matter (WM) structure in a group of 66 adults aged between 23 and 81. Multimodal imaging measures included voxel-based morphometry (VBM)-style analysis of GM and WM volume and diffusion tensor imaging (DTI) metrics of WM microstructure. We found widespread reductions in GM volume from middle age onwards but earlier reductions in GM were detected in frontal cortex. Widespread age-related deterioration in WM microstructure was detected from young adulthood onwards. WM decline was detected earlier and more sensitively using DTI-based measures of microstructure than using markers of WM volume derived from conventional T1-weighted imaging.}
}

% medical annotation
@article{Aljabri2022Towardsannotation,
  title={Towards a better understanding of annotation tools for medical imaging: a survey},
  author={Manar Aljabri and Manal Alamir and Manal Al Ghamdi and Mohamed Abdel-Mottaleb and Fernando Collado-Mesa},
  journal={Multimedia Tools and Applications},
  year={2022},
  volume={81},
  pages={25877 - 25911},
  url={https://api.semanticscholar.org/CorpusID:247711911}
}

%% improved ccgan
@ARTICLE{improved_ccgan,
    author={Ding, Xin and Wang, Yongwei and Xu, Zuheng and Welch, William J. and Wang, Z. Jane},
    journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, 
    title={Continuous Conditional Generative Adversarial Networks: Novel Empirical Losses and Label Input Mechanisms}, 
    year={2023},
    volume={45},
    number={7},
    pages={8143-8158},
    doi={10.1109/TPAMI.2022.3228915}
}

%% small hippocampus
@ARTICLE{cognitive_aging_hippocampus,
AUTHOR={O'Shea, Andrew and Cohen, Ronald and Porges, Eric and Nissim, Nicole and Woods, Adam},
TITLE={Cognitive Aging and the Hippocampus in Older Adults},
JOURNAL={Frontiers in Aging Neuroscience},
VOLUME={8},
YEAR={2016},
URL={https://www.frontiersin.org/articles/10.3389/fnagi.2016.00298},
DOI={10.3389/fnagi.2016.00298},
ISSN={1663-4365}, 
ABSTRACT={The hippocampus is one of the most well studied structures in the human brain. While age-related decline in hippocampal volume is well documented, most of our knowledge about hippocampal structure-function relationships was discovered in the context of neurological and neurodegenerative diseases. The relationship between cognitive aging and hippocampal structure in the absence of disease remains relatively understudied. Furthermore, the few studies that have investigated the role of the hippocampus in cognitive aging have produced contradictory results. To address these issues, we assessed 93 older adults from the general community (mean age = 71.9 ± 9.3 years) on the Montreal Cognitive Assessment (MoCA), a brief cognitive screening measure for dementia, and the NIH Toolbox-Cognitive Battery (NIHTB-CB), a computerized neurocognitive battery. High-resolution structural magnetic resonance imaging (MRI) was used to estimate hippocampal volume. Lower MoCA Total (p = 0.01) and NIHTB-CB Fluid Cognition (p < 0.001) scores were associated with decreased hippocampal volume, even while controlling for sex and years of education. Decreased hippocampal volume was significantly associated with decline in multiple NIHTB-CB subdomains, including episodic memory, working memory, processing speed and executive function. This study provides important insight into the multifaceted role of the hippocampus in cognitive aging.}
}

@article{subregion_hippocampus_EVANS2018129,
title = {Subregional volumes of the hippocampus in relation to cognitive function and risk of dementia},
journal = {NeuroImage},
volume = {178},
pages = {129-135},
year = {2018},
issn = {1053-8119},
doi = {https://doi.org/10.1016/j.neuroimage.2018.05.041},
url = {https://www.sciencedirect.com/science/article/pii/S1053811918304488},
author = {Tavia E. Evans and Hieab H.H. Adams and Silvan Licher and Frank J. Wolters and Aad {van der Lugt} and M. Kamran Ikram and Michael J. O'Sullivan and Meike W. Vernooij and M. Arfan Ikram},
keywords = {Dementia, Neuroimaging, Cognition, Hippocampus},
abstract = {Background
Total hippocampal volume has been consistently linked to cognitive function and dementia. Yet, given its complex and parcellated internal structure, the role of subregions of the hippocampus in cognition and risk of dementia remains relatively underexplored. We studied subregions of the hippocampus in a large population-based cohort to further understand their role in cognitive impairment and dementia risk.
Methods
We studied 5035 dementia- and stroke-free persons from the Rotterdam Study, aged over 45 years. All participants underwent magnetic resonance imaging (1.5 T) between 2005 and 2015. Automatic segmentation of the hippocampus and 12 of its subregions was performed using the FreeSurfer software (version 6.0). A cognitive test battery was performed, and participants were followed up for the development of dementia until 2015. Associations of hippocampal subregion volumes with cognition and incident dementia were examined using linear and Cox regression models, respectively. All analyses were adjusted for age, sex, education, and total hippocampal volume.
Results
Mean age was 64.3 years (SD 10.6) with 56% women. Smaller volumes of the hippocampal fimbria, presubiculum and subiculum showed the strongest associations with poor performance on several cognitive domains, including executive function but not memory. During a mean follow-up of 5.5 years, 76 persons developed dementia. Smaller subiculum volume was associated with risk of dementia adjusted for total volume (hazard ratio per SD decrease in volume: 1.75, 95% confidence interval 1.35; 2.26).
Conclusions
In a community-dwelling non-demented population, we describe patterns of association between hippocampal subregions with cognition and risk of dementia. Specifically, the subiculum was associated with both poorer cognition and higher risk of dementia.}
}