@article{RicciLara2022AddressingFI,
  title={Addressing fairness in artificial intelligence for medical imaging},
  author={Mar{\'i}a Agustina Ricci Lara and Rodrigo Echeveste and Enzo Ferrante},
  journal={Nature Communications},
  year={2022},
  volume={13},
  url={https://api.semanticscholar.org/CorpusID:251371910}
}

@ARTICLE{Diab2025-fm,
  title     = "Distinguishing between rigor and transparency in {FDA} marketing
               authorization of {AI-enabled} medical devices",
  author    = "Diab, Abdul Rahman and Lotter, William",
  abstract  = "The increasing prevalence of artificial intelligence
               (AI)-enabled medical devices presents significant opportunities
               for improving patient outcomes. However, recent studies based on
               public U.S. Food and Drug Administration (FDA) summaries have
               raised concerns about the extent of validation that such devices
               undergo before FDA marketing authorization and subsequent
               clinical deployment. Here, the authors clarify key concepts of
               FDA regulation and provide insights into the current standards
               of performance validation, focusing on radiology AI devices. The
               authors distinguish between two fundamentally different but
               often conflated concepts: validation rigor (ie, the quality and
               comprehensiveness of the evidence supporting a device's
               performance) and validation transparency (ie, the extent to
               which this evidence is publicly accessible). The authors begin
               by describing the inverse relationship between the amount of
               performance data contained and the transparency of specific
               components of an FDA submission. Drawing on FDA guidelines and
               on experience developing authorized AI devices, the authors
               outline current validation standards and present a mapping from
               common radiology AI device types to their typical clinical study
               designs. This article concludes with actionable recommendations,
               advocating for a balanced approach tailored to specific use
               cases while still enforcing certain universal standards. These
               measures will help ensure that AI-enabled medical devices are
               both rigorously evaluated and transparently reported, thereby
               fostering greater public trust and enhancing clinical utility.",
  journal   = "Radiol. Artif. Intell.",
  publisher = "Radiological Society of North America (RSNA)",
  volume    =  7,
  number    =  6,
  pages     = "e250369",
  month     =  nov,
  year      =  2025,
  keywords  = "Computer Aided Diagnosis; Computer Applications-General
               (Informatics); Health Policy and Practice; Informatics; Neural
               Networks; Technology Assessment",
  language  = "en"
}


@ARTICLE{Sultan2025-co,
  title     = "An unsupervised method for {MRI} recovery: deep image prior with
               structured sparsity",
  author    = "Sultan, Muhammad Ahmad and Chen, Chong and Liu, Yingmin and Gil,
               Katarzyna and Zareba, Karolina and Ahmad, Rizwan",
  abstract  = "OBJECTIVE: To propose and validate an unsupervised MRI
               reconstruction method that does not require fully sampled
               k-space data. MATERIALS AND METHODS: The proposed method, deep
               image prior with structured sparsity (DISCUS), extends the deep
               image prior (DIP) by introducing group sparsity to
               frame-specific code vectors, enabling the discovery of a
               low-dimensional manifold for capturing temporal variations.
               DISCUS was validated using four studies: (I) simulation of a
               dynamic Shepp-Logan phantom to demonstrate its manifold
               discovery capabilities, (II) comparison with compressed sensing
               and DIP-based methods using simulated single-shot late
               gadolinium enhancement (LGE) image series from six distinct
               digital cardiac phantoms in terms of normalized mean square
               error (NMSE) and structural similarity index measure (SSIM),
               (III) evaluation on retrospectively undersampled single-shot LGE
               data from eight patients, and (IV) evaluation on prospectively
               undersampled single-shot LGE data from eight patients, assessed
               via blind scoring from two expert readers. RESULTS: DISCUS
               outperformed competing methods, demonstrating superior
               reconstruction quality in terms of NMSE and SSIM (Studies I-III)
               and expert reader scoring (Study IV). DISCUSSION: An
               unsupervised image reconstruction method is presented and
               validated on simulated and measured data. These developments can
               benefit applications where acquiring fully sampled data is
               challenging.",
  journal   = "MAGMA",
  publisher = "Springer Science and Business Media LLC",
  volume    =  38,
  number    =  5,
  pages     = "859--871",
  month     =  oct,
  year      =  2025,
  keywords  = "Cardiac MRI; Reconstruction; Unsupervised learning",
  copyright = "https://creativecommons.org/licenses/by/4.0",
  language  = "en"
}

@ARTICLE{Chen2023-vn,
  title    = "Deep learning based unpaired image-to-image translation
              applications for medical physics: a systematic review",
  author   = "Chen, Junhua and Chen, Shenlun and Wee, Leonard and Dekker, Andre
              and Bermejo, Inigo",
  abstract = "Purpose. There is a growing number of publications on the
              application of unpaired image-to-image (I2I) translation in
              medical imaging. However, a systematic review covering the
              current state of this topic for medical physicists is lacking.
              The aim of this article is to provide a comprehensive review of
              current challenges and opportunities for medical physicists and
              engineers to apply I2I translation in practice.Methods and
              materials. The PubMed electronic database was searched using
              terms referring to unpaired (unsupervised), I2I translation, and
              medical imaging. This review has been reported in compliance with
              the Preferred Reporting Items for Systematic Reviews and
              Meta-Analyses (PRISMA) statement. From each full-text article, we
              extracted information extracted regarding technical and clinical
              applications of methods, Transparent Reporting for Individual
              Prognosis Or Diagnosis (TRIPOD) study type, performance of
              algorithm and accessibility of source code and pre-trained
              models.Results. Among 461 unique records, 55 full-text articles
              were included in the review. The major technical applications
              described in the selected literature are segmentation (26
              studies), unpaired domain adaptation (18 studies), and denoising
              (8 studies). In terms of clinical applications, unpaired I2I
              translation has been used for automatic contouring of regions of
              interest in MRI, CT, x-ray and ultrasound images, fast MRI or low
              dose CT imaging, CT or MRI only based radiotherapy planning, etc
              Only 5 studies validated their models using an independent test
              set and none were externally validated by independent
              researchers. Finally, 12 articles published their source code and
              only one study published their pre-trained models.Conclusion. I2I
              translation of medical images offers a range of valuable
              applications for medical physicists. However, the scarcity of
              external validation studies of I2I models and the shortage of
              publicly available pre-trained models limits the immediate
              applicability of the proposed methods in practice.",
  journal  = "Phys. Med. Biol.",
  volume   =  68,
  number   =  5,
  month    =  feb,
  year     =  2023,
  keywords = "image-to-image translation; medical imaging; systematic review;
              unpaired",
  language = "en"
}

@article{CHEN2026102015,
title = {A multi-dynamic low-rank deep image prior for three-dimensional real-time cardiovascular magnetic resonance imaging},
journal = {Journal of Cardiovascular Magnetic Resonance},
volume = {28},
number = {1},
pages = {102015},
year = {2026},
issn = {1097-6647},
doi = {https://doi.org/10.1016/j.jocmr.2025.102015},
url = {https://www.sciencedirect.com/science/article/pii/S1097664725001772},
author = {Chong Chen and Marc Vornehm and Zhenyu Bu and Preethi Chandrasekaran and Muhammad A. Sultan and Syed M. Arshad and Yingmin Liu and Yuchi Han and Rizwan Ahmad},
keywords = {3D CMR, Real-time, Accelerated, Deep image prior, Arrhythmia},
abstract = {ABSTRACT
Purpose
To develop a reconstruction framework for three-dimensional (3D) real-time cine cardiovascular magnetic resonance (CMR) from highly undersampled data without requiring fully sampled training datasets.
Methods
We developed a multi-dynamic low-rank deep image prior (ML-DIP) framework that models spatial image content and deformation fields using separate neural networks. These sub-networks are jointly trained per scan to reconstruct the dynamic image series directly from undersampled k-space data. ML-DIP was evaluated on (i) a 3D cine digital phantom with simulated premature ventricular contractions (PVCs), (ii) 10 healthy subjects (including 2 scanned during both rest and exercise), and (iii) 12 patients with a history of PVCs. Phantom results were assessed using peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM). In vivo performance was evaluated by comparing left-ventricular function quantification (against two-dimensional [2D] real-time cine) and image quality (against 2D real-time cine and binning-based five-dimensional cine [5D-Cine]).
Results
In the phantom study, ML-DIP achieved PSNR >29dB and SSIM >0.90 for scan times as short as 2min, while recovering cardiac motion, respiratory motion, and PVC events. In healthy subjects, ML-DIP yielded functional measurements comparable to 2D cine and higher image quality than 5D-Cine, including during exercise with high heart rates and bulk motion. In PVC patients, ML-DIP preserved beat-to-beat variations and reconstructed irregular beats, whereas 5D-Cine showed motion artifacts and information loss due to binning.
Conclusion
ML-DIP enables high-quality 3D real-time CMR with acceleration factors exceeding 1000 by learning low-rank spatial and motion representations from undersampled data, without relying on external fully sampled training datasets.}
}

@ARTICLE{Lee2024-ic,
  title     = "Image quality and diagnostic performance of low-dose liver {CT}
               with deep learning reconstruction versus standard-dose {CT}",
  author    = "Lee, Dong Ho and Lee, Jeong Min and Lee, Chang Hee and Afat,
               Saif and Othman, Ahmed",
  abstract  = "Purpose To compare the image quality and diagnostic capability
               in detecting malignant liver tumors of low-dose CT (LDCT, 33\%
               dose) with deep learning-based denoising (DLD) and standard-dose
               CT (SDCT, 100\% dose) with model-based iterative reconstruction
               (MBIR). Materials and Methods In this prospective, multicenter,
               noninferiority study, individuals referred for liver CT scans
               were enrolled from three tertiary referral hospitals between
               February 2021 and August 2022. All liver CT scans were conducted
               using a dual-source scanner with the dose split into tubes A
               (67\% dose) and B (33\% dose). Blended images from tubes A and B
               were created using MBIR to produce SDCT images, whereas LDCT
               images used data from tube B and were reconstructed with DLD.
               The noise in liver images was measured and compared between
               imaging techniques. The diagnostic performance of each technique
               in detecting malignant liver tumors was evaluated by three
               independent radiologists using jackknife alternative
               free-response receiver operating characteristic analysis.
               Noninferiority of LDCT compared with SDCT was declared when the
               lower limit of the 95\% CI for the difference in figure of merit
               (FOM) was greater than -0.10. Results A total of 296
               participants (196 men, 100 women; mean age, 60.5 years $\pm$
               13.3 [SD]) were included. The mean noise level in the liver was
               significantly lower for LDCT (10.1) compared with SDCT (10.7) (P
               < .001). Diagnostic performance was assessed in 246 participants
               (108 malignant tumors in 90 participants). The reader-averaged
               FOM was 0.880 for SDCT and 0.875 for LDCT (P = .35). The
               difference fell within the noninferiority margin (difference,
               -0.005 [95\% CI: -0.024, 0.012]). Conclusion Compared with SDCT
               with MBIR, LDCT using 33\% of the standard radiation dose had
               reduced image noise and comparable diagnostic performance in
               detecting malignant liver tumors. Keywords: CT, Abdomen/GI,
               Liver, Comparative Studies, Diagnosis, Reconstruction Algorithms
               Clinical trial registration no. NCT05804799 \copyright{} RSNA,
               2024 Supplemental material is available for this article.",
  journal   = "Radiol. Artif. Intell.",
  publisher = "Radiological Society of North America (RSNA)",
  volume    =  6,
  number    =  2,
  pages     = "e230192",
  month     =  mar,
  year      =  2024,
  keywords  = "Abdomen/GI; CT; Comparative Studies; Diagnosis; Liver;
               Reconstruction Algorithms",
  language  = "en"
}


% The entry below contains non-ASCII chars that could not be converted
% to a LaTeX equivalent.
@ARTICLE{Feuerriegel2023-rt,
  title    = "Evaluation of a deep learning-based reconstruction method for
              denoising and image enhancement of shoulder {MRI} in patients
              with shoulder pain",
  author   = "Feuerriegel, Georg C and Weiss, Kilian and Kronthaler, Sophia and
              Leonhardt, Yannik and Neumann, Jan and Wurm, Markus and Lenhart,
              Nicolas S and Makowski, Marcus R and Schwaiger, Benedikt J and
              Woertler, Klaus and Karampinos, Dimitrios C and Gersing,
              Alexandra S",
  journal  = "Eur. Radiol.",
  volume   =  33,
  number   =  7,
  pages    = "4875--4884",
  month    =  jul,
  year     =  2023,
  keywords = "Compressed SENSE; Deep learning algorithm; Magnetic resonance
              imaging; Shoulder injury",
  language = "en"
}


@article{vokinger2021mitigating,
  title={Mitigating bias in machine learning for medicine},
  author={Vokinger, Kerstin N and Feuerriegel, Stefan and Kesselheim, Aaron S},
  journal={Communications Medicine},
  volume={1},
  pages={25},
  year={2021},
  publisher={Nature Publishing Group},
  doi={10.1038/s43856-021-00028-w},
  pmid={34522916},
  pmcid={PMC7611652}
}

@article{Lin21FDA,
author = {Lin, MingDe},
year = {2021},
month = {11},
pages = {},
title = {What's Needed to Bridge the Gap Between US FDA Clearance and Real-world Use of AI Algorithms},
volume = {29},
journal = {Academic radiology},
doi = {10.1016/j.acra.2021.10.007}
}

@article{SENTHIL2024100182,
title = {Bibliometric analysis of artificial intelligence in healthcare research: Trends and future directions},
journal = {Future Healthcare Journal},
volume = {11},
number = {3},
pages = {100182},
year = {2024},
issn = {2514-6645},
doi = {https://doi.org/10.1016/j.fhj.2024.100182},
url = {https://www.sciencedirect.com/science/article/pii/S2514664524015728},
author = {Renganathan Senthil and Thirunavukarasou Anand and Chaitanya Sree Somala and Konda Mani Saravanan},
keywords = {Bibliometric analysis, Artificial intelligence, COVID-19, Scientific production, Emerging trends}
}

@article{Esteva21DLCV,
author = {Esteva, Andre and Chou, Katherine and Yeung, Serena and Naik, Nikhil and Madani, Ali and Mottaghi, Ali and Liu, Yun and Topol, Eric and Dean, Jeff and Socher, Richard},
year = {2021},
month = {12},
pages = {5},
title = {Deep learning-enabled medical computer vision},
volume = {4},
journal = {npj Digital Medicine},
doi = {10.1038/s41746-020-00376-2}
}

@ARTICLE{DBLP:journals/corr/abs-2110-00603,
  title    = "Algorithmic fairness in artificial intelligence for medicine and
              healthcare",
  author   = "Chen, Richard J and Wang, Judy J and Williamson, Drew F K and
              Chen, Tiffany Y and Lipkova, Jana and Lu, Ming Y and Sahai,
              Sharifa and Mahmood, Faisal",
  abstract = "In healthcare, the development and deployment of insufficiently
              fair systems of artificial intelligence (AI) can undermine the
              delivery of equitable care. Assessments of AI models stratified
              across subpopulations have revealed inequalities in how patients
              are diagnosed, treated and billed. In this Perspective, we
              outline fairness in machine learning through the lens of
              healthcare, and discuss how algorithmic biases (in data
              acquisition, genetic variation and intra-observer labelling
              variability, in particular) arise in clinical workflows and the
              resulting healthcare disparities. We also review emerging
              technology for mitigating biases via disentanglement, federated
              learning and model explainability, and their role in the
              development of AI-based software as a medical device.",
  journal  = "Nat. Biomed. Eng.",
  volume   =  7,
  number   =  6,
  pages    = "719--742",
  month    =  jun,
  year     =  2023,
  language = "en"
}

@article{Burlina2021Retinal,
author = {Burlina, Philippe and Joshi, Neil and Paul, William and Pacheco, Katia and Bressler, Neil},
year = {2021},
month = {02},
pages = {13},
title = {Addressing Artificial Intelligence Bias in Retinal Diagnostics},
volume = {10},
journal = {Translational Vision Science  Technology},
doi = {10.1167/tvst.10.2.13}
}

@article{Kalantari2021UnderdiagnosisCheX,
author = {Seyyed-Kalantari, Laleh and Zhang, Haoran and McDermott, Matthew and Chen, Irene and Ghassemi, Marzyeh},
year = {2021},
month = {12},
pages = {},
title = {Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populations},
volume = {27},
journal = {Nature Medicine},
doi = {10.1038/s41591-021-01595-0}
}

@article{Stanley2022FairnessrelatedPA,
  title={Fairness-related performance and explainability effects in deep learning models for brain image analysis},
  author={Emma A. M. Stanley and Matthias Wilms and Pauline Mouches and Nils Daniel Forkert},
  journal={Journal of Medical Imaging},
  year={2022},
  volume={9},
  pages={061102 - 061102},
  url={https://api.semanticscholar.org/CorpusID:251876386}
}
@article{saumure2025humor,
  title={Humor as a window into generative AI bias},
  author={Saumure, R. and De Freitas, J. and Puntoni, S.},
  journal={Scientific Reports},
  volume={15},
  number={1},
  pages={1326},
  year={2025},
  doi={10.1038/s41598-024-83384-6},
  url={https://doi.org/10.1038/s41598-024-83384-6}
}

@inproceedings{ruggeri-nozza-2023-multi,
    title = "A Multi-dimensional study on Bias in Vision-Language models",
    author = "Ruggeri, Gabriele  and
      Nozza, Debora",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.findings-acl.403/",
    doi = "10.18653/v1/2023.findings-acl.403",
    pages = "6445--6455",
}

@inproceedings{luccioni2023stablebiasanalyzingsocietal,
author = {Luccioni, Alexandra Sasha and Akiki, Christopher and Mitchell, Margaret and Jernite, Yacine},
title = {Stable bias: evaluating societal representations in diffusion models},
year = {2023},
publisher = {Curran Associates Inc.},
address = {Red Hook, NY, USA},
abstract = {As machine learning-enabled Text-to-Image (TTI) systems are becoming increasingly prevalent and seeing growing adoption as commercial services, characterizing the social biases they exhibit is a necessary first step to lowering their risk of discriminatory outcomes. This evaluation, however, is made more difficult by the synthetic nature of these systems' outputs: common definitions of diversity are grounded in social categories of people living in the world, whereas the artificial depictions of fictive humans created by these systems have no inherent gender or ethnicity. To address this need, we propose a new method for exploring the social biases in TTI systems. Our approach relies on characterizing the variation in generated images triggered by enumerating gender and ethnicity markers in the prompts, and comparing it to the variation engendered by spanning different professions. This allows us to (1) identify specific bias trends, (2) provide targeted scores to directly compare models in terms of diversity and representation, and (3) jointly model interdependent social variables to support a multidimensional analysis. We leverage this method to analyze images generated by 3 popular TTI systems (Dall E 2, Stable Diffusion v 1.4 and 2) and find that while all of their outputs show correlations with US labor demographics, they also consistently under-represent marginalized identities to different extents. We also release the datasets and low-code interactive bias exploration platforms developed for this work, as well as the necessary tools to similarly evaluate additional TTI systems.},
booktitle = {Proceedings of the 37th International Conference on Neural Information Processing Systems},
articleno = {2458},
numpages = {14},
location = {New Orleans, LA, USA},
series = {NIPS '23}
}

@article{cross2024bias,
  title={Bias in medical AI: Implications for clinical decision-making},
  author={Cross, JL and Choma, MA and Onofrey, JA},
  journal={PLOS Digital Health},
  volume={3},
  number={11},
  pages={e0000651},
  year={2024},
  month={Nov},
  doi={10.1371/journal.pdig.0000651},
  pmid={39509461},
  pmcid={PMC11542778}
}

@article{CHIU2024103188,
title = {Achieve fairness without demographics for dermatological disease diagnosis},
journal = {Medical Image Analysis},
volume = {95},
pages = {103188},
year = {2024},
issn = {1361-8415},
doi = {https://doi.org/10.1016/j.media.2024.103188},
url = {https://www.sciencedirect.com/science/article/pii/S1361841524001130},
author = {Ching-Hao Chiu and Yu-Jen Chen and Yawen Wu and Yiyu Shi and Tsung-Yi Ho},
keywords = {Dermatological disease diagnosis, AI fairness, Fairness through unawareness},
}

@article{Glocker2023AlgorithmicEO,
  title={Algorithmic encoding of protected characteristics in chest X-ray disease detection models},
  author={Ben Glocker and Charles Jones and M{\'e}lanie Bernhardt and Stefan Winzeck},
  journal={eBioMedicine},
  year={2023},
  volume={89},
  url={https://api.semanticscholar.org/CorpusID:256858498}
}

@article{WANG2024105047,
title = {Drop the shortcuts: image augmentation improves fairness and decreases AI detection of race and other demographics from medical images},
journal = {eBioMedicine},
volume = {102},
pages = {105047},
year = {2024},
issn = {2352-3964},
doi = {https://doi.org/10.1016/j.ebiom.2024.105047},
url = {https://www.sciencedirect.com/science/article/pii/S2352396424000823},
author = {Ryan Wang and Po-Chih Kuo and Li-Ching Chen and Kenneth Patrick Seastedt and Judy Wawira Gichoya and Leo Anthony Celi},
keywords = {Bias mitigation, Shortcuts, Augmentation, Fairness, Deep learning},
}

@inproceedings{ioannou2022studydemographicbiascnnbased,
author = {Ioannou, Stefanos and Chockler, Hana and Hammers, Alexander and King, Andrew P.},
title = {A Study of Demographic Bias in CNN-Based Brain MR Segmentation},
year = {2022},
isbn = {978-3-031-17898-6},
publisher = {Springer-Verlag},
address = {Berlin, Heidelberg},
url = {https://doi.org/10.1007/978-3-031-17899-3_2},
doi = {10.1007/978-3-031-17899-3_2},
abstract = {Convolutional neural networks (CNNs) are increasingly being used to automate the segmentation of brain structures in magnetic resonance (MR) images for research studies. In other applications, CNN models have been shown to exhibit bias against certain demographic groups when they are under-represented in the training sets. In this work, we investigate whether CNN models for brain MR segmentation have the potential to contain sex or race bias when trained with imbalanced training sets. We train multiple instances of the FastSurferCNN model using different levels of sex imbalance in white subjects. We evaluate the performance of these models separately for white male and white female test sets to assess sex bias, and furthermore evaluate them on black male and black female test sets to assess potential racial bias. We find significant sex and race bias effects in segmentation model performance. The biases have a strong spatial component, with some brain regions exhibiting much stronger bias than others. Overall, our results suggest that race bias is more significant than sex bias. Our study demonstrates the importance of considering race and sex balance when forming training sets for CNN-based brain MR segmentation, to avoid maintaining or even exacerbating existing health inequalities through biased research study findings.},
booktitle = {Machine Learning in Clinical Neuroimaging: 5th International Workshop, MLCN 2022, Held in Conjunction with MICCAI 2022, Singapore, September 18, 2022, Proceedings},
pages = {13–22},
numpages = {10},
keywords = {Brain, MR, Deep learning, Bias, Fairness},
location = {Singapore, Singapore}
}

@inproceedings{lee2022systematicstudyracesex,
author = {Lee, Tiarna and Puyol-Ant\'{o}n, Esther and Ruijsink, Bram and Shi, Miaojing and King, Andrew P.},
title = {A Systematic Study of Race and Sex Bias in CNN-Based Cardiac MR Segmentation},
year = {2022},
isbn = {978-3-031-23442-2},
publisher = {Springer-Verlag},
address = {Berlin, Heidelberg},
url = {https://doi.org/10.1007/978-3-031-23443-9_22},
doi = {10.1007/978-3-031-23443-9_22},
abstract = {In computer vision there has been significant research interest in assessing potential demographic bias in deep learning models. One of the main causes of such bias is imbalance in the training data. In medical imaging, where the potential impact of bias is arguably much greater, there has been less interest. In medical imaging pipelines, segmentation of structures of interest plays an important role in estimating clinical biomarkers that are subsequently used to inform patient management. Convolutional neural networks (CNNs) are starting to be used to automate this process. We present the first systematic study of the impact of training set imbalance on race and sex bias in CNN-based segmentation. We focus on segmentation of the structures of the heart from short axis cine cardiac magnetic resonance images, and train multiple CNN segmentation models with different levels of race/sex imbalance. We find no significant bias in the sex experiment but significant bias in two separate race experiments, highlighting the need to consider adequate representation of different demographic groups in health datasets.},
booktitle = {Statistical Atlases and Computational Models of the Heart. Regular and CMRxMotion Challenge Papers: 13th International Workshop, STACOM 2022, Held in Conjunction with MICCAI 2022},
pages = {233–244},
numpages = {12},
keywords = {Segmentation, Fairness, CNN, Cardiac MRI},
location = {Singapore, Singapore}
}

@article{puyol2022fairness,
  title={Fairness in Cardiac Magnetic Resonance Imaging: Assessing Sex and Racial Bias in Deep Learning-Based Segmentation},
  author={Puyol-Ant{\'o}n, E and Ruijsink, B and Mariscal Harana, J and Piechnik, SK and Neubauer, S and Petersen, SE and Razavi, R and Chowienczyk, P and King, AP},
  journal={Frontiers in Cardiovascular Medicine},
  volume={9},
  pages={859310},
  year={2022},
  month={Apr},
  doi={10.3389/fcvm.2022.859310},
  pmid={35463778},
  pmcid={PMC9021445}
}

@inproceedings{du2023unveilingfairnessbiasesdeep,
author = {Du, Yuning and Xue, Yuyang and Dharmakumar, Rohan and Tsaftaris, Sotirios A.},
title = {Unveiling Fairness Biases in Deep Learning-Based Brain MRI Reconstruction},
year = {2023},
isbn = {978-3-031-45248-2},
publisher = {Springer-Verlag},
address = {Berlin, Heidelberg},
url = {https://doi.org/10.1007/978-3-031-45249-9_10},
doi = {10.1007/978-3-031-45249-9_10},
abstract = {Deep learning (DL) reconstruction particularly of MRI has led to improvements in image fidelity and reduction of acquisition time. In neuroimaging, DL methods can reconstruct high-quality images from undersampled data. However, it is essential to consider fairness in DL algorithms, particularly in terms of demographic characteristics. This study presents the first fairness analysis in a DL-based brain MRI reconstruction model. The model utilises the U-Net architecture for image reconstruction and explores the presence and sources of unfairness by implementing baseline Empirical Risk Minimisation (ERM) and rebalancing strategies. Model performance is evaluated using image reconstruction metrics. Our findings reveal statistically significant performance biases between the gender and age subgroups. Surprisingly, data imbalance and training discrimination are not the main sources of bias. This analysis provides insights of fairness in DL-based image reconstruction and aims to improve equity in medical AI applications.},
booktitle = {Clinical Image-Based Procedures,  Fairness of AI in Medical Imaging, and Ethical and Philosophical Issues in Medical Imaging: 12th International Workshop, CLIP 2023 1st International Workshop, FAIMI 2023 and 2nd International Workshop, EPIMI 2023},
pages = {102–111},
numpages = {10},
keywords = {Fairness, Image Reconstruction, Algorithm Bias, Neuroimaging},
location = {Vancouver, BC, Canada}
}

@inproceedings{Sheg24reconbias,
author = {Sheng, Yi and Yang, Junhuan and Lin, Youzuo and Jiang, Weiwen and Yang, Lei},
title = {Toward Fair Ultrasound Computing Tomography: Challenges, Solutions and Outlook},
year = {2024},
isbn = {9798400706059},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3649476.3660387},
doi = {10.1145/3649476.3660387},
booktitle = {Proceedings of the Great Lakes Symposium on VLSI 2024},
pages = {748–753},
numpages = {6},
keywords = {Fairness, Image Reconstruction, InversionNet, USCT},
location = {Clearwater, FL, USA},
series = {GLSVLSI '24}
}

@Article{xu2024addressingfairnessissuesdeep,
author={Xu, Zikang
and Li, Jun
and Yao, Qingsong
and Li, Han
and Zhao, Mingyue
and Zhou, S. Kevin},
title={Addressing fairness issues in deep learning-based medical image analysis: a systematic review},
journal={npj Digital Medicine},
year={2024},
month={Oct},
day={17},
volume={7},
number={1},
pages={286},
abstract={Deep learning algorithms have demonstrated remarkable efficacy in various medical image analysis (MedIA) applications. However, recent research highlights a performance disparity in these algorithms when applied to specific subgroups, such as exhibiting poorer predictive performance in elderly females. Addressing this fairness issue has become a collaborative effort involving AI scientists and clinicians seeking to understand its origins and develop solutions for mitigation within MedIA. In this survey, we thoroughly examine the current advancements in addressing fairness issues in MedIA, focusing on methodological approaches. We introduce the basics of group fairness and subsequently categorize studies on fair MedIA into fairness evaluation and unfairness mitigation. Detailed methods employed in these studies are presented too. Our survey concludes with a discussion of existing challenges and opportunities in establishing a fair MedIA and healthcare system. By offering this comprehensive review, we aim to foster a shared understanding of fairness among AI researchers and clinicians, enhance the development of unfairness mitigation methods, and contribute to the creation of an equitable MedIA society.},
issn={2398-6352},
doi={10.1038/s41746-024-01276-5},
url={https://doi.org/10.1038/s41746-024-01276-5}
}



@article{mehrabi2022surveybiasfairnessmachine,
author = {Mehrabi, Ninareh and Morstatter, Fred and Saxena, Nripsuta and Lerman, Kristina and Galstyan, Aram},
title = {A Survey on Bias and Fairness in Machine Learning},
year = {2021},
issue_date = {July 2022},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {54},
number = {6},
issn = {0360-0300},
url = {https://doi.org/10.1145/3457607},
doi = {10.1145/3457607},
abstract = {With the widespread use of artificial intelligence (AI) systems and applications in our everyday lives, accounting for fairness has gained significant importance in designing and engineering of such systems. AI systems can be used in many sensitive environments to make important and life-changing decisions; thus, it is crucial to ensure that these decisions do not reflect discriminatory behavior toward certain groups or populations. More recently some work has been developed in traditional machine learning and deep learning that address such challenges in different subdomains. With the commercialization of these systems, researchers are becoming more aware of the biases that these applications can contain and are attempting to address them. In this survey, we investigated different real-world applications that have shown biases in various ways, and we listed different sources of biases that can affect AI applications. We then created a taxonomy for fairness definitions that machine learning researchers have defined to avoid the existing bias in AI systems. In addition to that, we examined different domains and subdomains in AI showing what researchers have observed with regard to unfair outcomes in the state-of-the-art methods and ways they have tried to address them. There are still many future directions and solutions that can be taken to mitigate the problem of bias in AI systems. We are hoping that this survey will motivate researchers to tackle these issues in the near future by observing existing work in their respective fields.},
journal = {ACM Comput. Surv.},
month = jul,
articleno = {115},
numpages = {35},
keywords = {representation learning, natural language processing, machine learning, deep learning, Fairness and bias in artificial intelligence}
}
@inproceedings{DBLP:journals/corr/HardtPS16,
author = {Hardt, Moritz and Price, Eric and Srebro, Nathan},
title = {Equality of opportunity in supervised learning},
year = {2016},
isbn = {9781510838819},
publisher = {Curran Associates Inc.},
address = {Red Hook, NY, USA},
abstract = {We propose a criterion for discrimination against a specified sensitive attribute in supervised learning, where the goal is to predict some target based on available features. Assuming data about the predictor, target, and membership in the protected group are available, we show how to optimally adjust any learned predictor so as to remove discrimination according to our definition. Our framework also improves incentives by shifting the cost of poor classification from disadvantaged groups to the decision maker, who can respond by improving the classification accuracy. We enourage readers to consult the more complete manuscript on the arXiv.},
booktitle = {Proceedings of the 30th International Conference on Neural Information Processing Systems},
pages = {3323–3331},
numpages = {9},
location = {Barcelona, Spain},
series = {NIPS'16}
}

@inproceedings{dwork2011fairnessawareness,
author = {Dwork, Cynthia and Hardt, Moritz and Pitassi, Toniann and Reingold, Omer and Zemel, Richard},
title = {Fairness through awareness},
year = {2012},
isbn = {9781450311151},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2090236.2090255},
doi = {10.1145/2090236.2090255},
abstract = {We study fairness in classification, where individuals are classified, e.g., admitted to a university, and the goal is to prevent discrimination against individuals based on their membership in some group, while maintaining utility for the classifier (the university). The main conceptual contribution of this paper is a framework for fair classification comprising (1) a (hypothetical) task-specific metric for determining the degree to which individuals are similar with respect to the classification task at hand; (2) an algorithm for maximizing utility subject to the fairness constraint, that similar individuals are treated similarly. We also present an adaptation of our approach to achieve the complementary goal of "fair affirmative action," which guarantees statistical parity (i.e., the demographics of the set of individuals receiving any classification are the same as the demographics of the underlying population), while treating similar individuals as similarly as possible. Finally, we discuss the relationship of fairness to privacy: when fairness implies privacy, and how tools developed in the context of differential privacy may be applied to fairness.},
booktitle = {Proceedings of the 3rd Innovations in Theoretical Computer Science Conference},
pages = {214–226},
numpages = {13},
location = {Cambridge, Massachusetts},
series = {ITCS '12}
}

@article{berk2017fairnesscriminaljusticerisk,
author = {Richard Berk and Hoda Heidari and Shahin Jabbari and Michael Kearns and Aaron Roth},
title ={Fairness in Criminal Justice Risk Assessments: The State of the Art},

journal = {Sociological Methods \& Research},
volume = {50},
number = {1},
pages = {3-44},
year = {2021},
doi = {10.1177/0049124118782533},

URL = { 
    
        https://doi.org/10.1177/0049124118782533
    
    

},
eprint = { 
    
        https://doi.org/10.1177/0049124118782533
    
    

}
,
    abstract = { Objectives: Discussions of fairness in criminal justice risk assessments typically lack conceptual precision. Rhetoric too often substitutes for careful analysis. In this article, we seek to clarify the trade-offs between different kinds of fairness and between fairness and accuracy.Methods: We draw on the existing literatures in criminology, computer science, and statistics to provide an integrated examination of fairness and accuracy in criminal justice risk assessments. We also provide an empirical illustration using data from arraignments.Results: We show that there are at least six kinds of fairness, some of which are incompatible with one another and with accuracy.Conclusions: Except in trivial cases, it is impossible to maximize accuracy and fairness at the same time and impossible simultaneously to satisfy all kinds of fairness. In practice, a major complication is different base rates across different legally protected groups. There is a need to consider challenging trade-offs. These lessons apply to applications well beyond criminology where assessments of risk can be used by decision makers. Examples include mortgage lending, employment, college admissions, child welfare, and medical diagnoses. }
}

@InProceedings{Kinyanyui2020Dermatology,
author="Kinyanjui, Newton M.
and Odonga, Timothy
and Cintas, Celia
and Codella, Noel C. F.
and Panda, Rameswar
and Sattigeri, Prasanna
and Varshney, Kush R.",
title="Fairness of Classifiers Across Skin Tones in Dermatology",
booktitle="Medical Image Computing and Computer Assisted Intervention -- MICCAI 2020",
year="2020",
publisher="Springer International Publishing",
address="Cham",
pages="320--329",
abstract="Recent advances in computer vision have led to breakthroughs in the development of automated skin image analysis. However, no attempt has been made to evaluate the consistency in performance across populations with varying skin tones. In this paper, we present an approach to estimate skin tone in skin disease benchmark datasets and investigate whether model performance is dependent on this measure. Specifically, we use individual typology angle (ITA) to approximate skin tone in dermatology datasets. We look at the distribution of ITA values to better understand skin color representation in two benchmark datasets: 1) the ISIC 2018 Challenge dataset, a collection of dermoscopic images of skin lesions for the detection of skin cancer, and 2) the SD-198 dataset, a collection of clinical images capturing a wide variety of skin diseases. To estimate ITA, we first develop segmentation models to isolate non-diseased areas of skin. We find that the majority of the data in the two datasets have ITA values between 34.5{\$}{\$}^{\backslash}circ {\$}{\$}∘and 48{\$}{\$}^{\backslash}circ {\$}{\$}∘, which are associated with lighter skin, and is consistent with under-representation of darker skinned populations in these datasets. We also find no measurable correlation between accuracy of machine learning models and ITA values, though more comprehensive data is needed for further validation.",
isbn="978-3-030-59725-2"
}

@InProceedings{densenet,
author = {Huang, Gao and Liu, Zhuang and van der Maaten, Laurens and Weinberger, Kilian Q.},
title = {Densely Connected Convolutional Networks},
booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {July},
year = {2017}
}

@article{resnet,
  author       = {Kaiming He and
                  Xiangyu Zhang and
                  Shaoqing Ren and
                  Jian Sun},
  title        = {Deep Residual Learning for Image Recognition},
  journal      = {arXiv},
  volume       = {abs/1512.03385},
  year         = {2015},
  url          = {http://arxiv.org/abs/1512.03385},
  eprinttype    = {arXiv},
  eprint       = {1512.03385},
  timestamp    = {Wed, 25 Jan 2023 11:01:16 +0100},
  biburl       = {https://dblp.org/rec/journals/corr/HeZRS15.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@article{jamanetworkopen.2023.42203,
    author = {Yuan, Chenxi and Linn, Kristin A. and Hubbard, Rebecca A.},
    title = {Algorithmic Fairness of Machine Learning Models for Alzheimer Disease Progression},
    journal = {JAMA Network Open},
    volume = {6},
    number = {11},
    pages = {e2342203-e2342203},
    year = {2023},
    month = {11},
    abstract = {Predictive models using machine learning techniques have potential to improve early detection and management of Alzheimer disease (AD). However, these models potentially have biases and may perpetuate or exacerbate existing disparities.To characterize the algorithmic fairness of longitudinal prediction models for AD progression.This prognostic study investigated the algorithmic fairness of logistic regression, support vector machines, and recurrent neural networks for predicting progression to mild cognitive impairment (MCI) and AD using data from participants in the Alzheimer Disease Neuroimaging Initiative evaluated at 57 sites in the US and Canada. Participants aged 54 to 91 years who contributed data on at least 2 visits between September 2005 and May 2017 were included. Data were analyzed in October 2022.Fairness was quantified across sex, ethnicity, and race groups. Neuropsychological test scores, anatomical features from T1 magnetic resonance imaging, measures extracted from positron emission tomography, and cerebrospinal fluid biomarkers were included as predictors.Outcome measures quantified fairness of prediction models (logistic regression [LR], support vector machine [SVM], and recurrent neural network [RNN] models), including equal opportunity, equalized odds, and demographic parity. Specifically, if the model exhibited equal sensitivity for all groups, it aligned with the principle of equal opportunity, indicating fairness in predictive performance.A total of 1730 participants in the cohort (mean [SD] age, 73.81 [6.92] years; 776 females [44.9\%]; 69 Hispanic [4.0\%] and 1661 non-Hispanic [96.0\%]; 29 Asian [1.7\%], 77 Black [4.5\%], 1599 White [92.4\%], and 25 other race [1.4\%]) were included. Sensitivity for predicting progression to MCI and AD was lower for Hispanic participants compared with non-Hispanic participants; the difference (SD) in true positive rate ranged from 20.9\% (5.5\%) for the RNN model to 27.8\% (9.8\%) for the SVM model in MCI and 24.1\% (5.4\%) for the RNN model to 48.2\% (17.3\%) for the LR model in AD. Sensitivity was similarly lower for Black and Asian participants compared with non-Hispanic White participants; for example, the difference (SD) in AD true positive rate was 14.5\% (51.6\%) in the LR model, 12.3\% (35.1\%) in the SVM model, and 28.4\% (16.8\%) in the RNN model for Black vs White participants, and the difference (SD) in MCI true positive rate was 25.6\% (13.1\%) in the LR model, 24.3\% (13.1\%) in the SVM model, and 6.8\% (18.7\%) in the RNN model for Asian vs White participants. Models generally satisfied metrics of fairness with respect to sex, with no significant differences by group, except for cognitively normal (CN)–MCI and MCI-AD transitions (eg, an absolute increase [SD] in the true positive rate of CN-MCI transitions of 10.3\% [27.8\%] for the LR model).In this study, models were accurate in aggregate but failed to satisfy fairness metrics. These findings suggest that fairness should be considered in the development and use of machine learning models for AD progression.},
    issn = {2574-3805},
    doi = {10.1001/jamanetworkopen.2023.42203},
    url = {https://doi.org/10.1001/jamanetworkopen.2023.42203},
    eprint = {https://jamanetwork.com/journals/jamanetworkopen/articlepdf/2811461/yuan\_2023\_oi\_231221\_1698415358.87588.pdf},
}


@inproceedings{Oguguo23,
  author={Oguguo, Tochi and Zamzmi, Ghada and Rajaraman, Sivaramakrishnan and Yang, Feng and Xue, Zhiyun and Antani, Sameer},
  booktitle={2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI)}, 
  title={A Comparative Study of Fairness in Medical Machine Learning}, 
  year={2023},
  volume={},
  number={},
  pages={1-5},
  keywords={Image segmentation;Machine learning algorithms;Lung;Machine learning;Medical services;Lesions;Task analysis;Medical machine learning;Responsible machine learning;fairness;medical image analysis},
  doi={10.1109/ISBI53787.2023.10230368}}


@INPROCEEDINGS {bissoto2019deconstructingbiasskinlesion,
author = { Bissoto, Alceu and Fornaciali, Michel and Valle, Eduardo and Avila, Sandra },
booktitle = { 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) },
title = {{ (De) Constructing Bias on Skin Lesion Datasets }},
year = {2019},
volume = {},
ISSN = {},
pages = {2766-2774},
abstract = { Melanoma is the deadliest form of skin cancer. Automated skin lesion analysis plays an important role for early detection. Nowadays, the ISIC Archive and the Atlas of Dermoscopy dataset are the most employed skin lesion sources to benchmark deep-learning based tools. However, all datasets contain biases, often unintentional, due to how they were acquired and annotated. Those biases distort the performance of machine-learning models, creating spurious correlations that the models can unfairly exploit, or, contrarily destroying cogent correlations that the models could learn. In this paper, we propose a set of experiments that reveal both types of biases, positive and negative, in existing skin lesion datasets. Our results show that models can correctly classify skin lesion images without clinically-meaningful information: disturbingly, the machine-learning model learned over images where no information about the lesion remains, presents an accuracy above the AI benchmark curated with dermatologists' performances. That strongly suggests spurious correlations guiding the models. We fed models with additional clinically meaningful information, which failed to improve the results even slightly, suggesting the destruction of cogent correlations. Our main findings raise awareness of the limitations of models trained and evaluated in small datasets such as the ones we evaluated, and may suggest future guidelines for models intended for real-world deployment. },
keywords = {Lesions;Skin;Correlation;Medical diagnostic imaging;Melanoma;Machine learning},
doi = {10.1109/CVPRW.2019.00335},
url = {https://doi.ieeecomputersociety.org/10.1109/CVPRW.2019.00335},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
month =Jun}

@InProceedings{marcinkevičs2022debiasingdeepchestxray,
  title = 	 {Debiasing Deep Chest {X}-Ray Classifiers using Intra- and Post-processing Methods},
  author =       {Marcinkevics, Ricards and Ozkan, Ece and Vogt, Julia E.},
  booktitle = 	 {Proceedings of the 7th Machine Learning for Healthcare Conference},
  pages = 	 {504--536},
  year = 	 {2022},
  volume = 	 {182},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {05--06 Aug},
  publisher =    {PMLR},
  pdf = 	 {https://proceedings.mlr.press/v182/marcinkevics22a/marcinkevics22a.pdf},
  url = 	 {https://proceedings.mlr.press/v182/marcinkevics22a.html},
}

@ARTICLE{Yang2024-dm,
  title     = "The limits of fair medical imaging {AI} in real-world
               generalization",
  author    = "Yang, Yuzhe and Zhang, Haoran and Gichoya, Judy W and Katabi,
               Dina and Ghassemi, Marzyeh",
  abstract  = "As artificial intelligence (AI) rapidly approaches human-level
               performance in medical imaging, it is crucial that it does not
               exacerbate or propagate healthcare disparities. Previous
               research established AI's capacity to infer demographic data
               from chest X-rays, leading to a key concern: do models using
               demographic shortcuts have unfair predictions across
               subpopulations? In this study, we conducted a thorough
               investigation into the extent to which medical AI uses
               demographic encodings, focusing on potential fairness
               discrepancies within both in-distribution training sets and
               external test sets. Our analysis covers three key medical
               imaging disciplines-radiology, dermatology and ophthalmology-and
               incorporates data from six global chest X-ray datasets. We
               confirm that medical imaging AI leverages demographic shortcuts
               in disease classification. Although correcting shortcuts
               algorithmically effectively addresses fairness gaps to create
               'locally optimal' models within the original data distribution,
               this optimality is not true in new test settings. Surprisingly,
               we found that models with less encoding of demographic
               attributes are often most 'globally optimal', exhibiting better
               fairness during model evaluation in new test environments. Our
               work establishes best practices for medical imaging models that
               maintain their performance and fairness in deployments beyond
               their initial training contexts, underscoring critical
               considerations for AI clinical deployments across populations
               and sites.",
  journal   = "Nat. Med.",
  publisher = "Springer Science and Business Media LLC",
  volume    =  30,
  number    =  10,
  pages     = "2838--2848",
  month     =  oct,
  year      =  2024,
  copyright = "https://creativecommons.org/licenses/by/4.0",
  language  = "en"
}

@ARTICLE{Lotter2024-sk,
  title     = "Acquisition parameters influence {AI} recognition of race in
               chest x-rays and mitigating these factors reduces underdiagnosis
               bias",
  author    = "Lotter, William",
  abstract  = "A core motivation for the use of artificial intelligence (AI) in
               medicine is to reduce existing healthcare disparities. Yet,
               recent studies have demonstrated two distinct findings: (1) AI
               models can show performance biases in underserved populations,
               and (2) these same models can be directly trained to recognize
               patient demographics, such as predicting self-reported race from
               medical images alone. Here, we investigate how these findings
               may be related, with an end goal of reducing a previously
               identified underdiagnosis bias. Using two popular chest x-ray
               datasets, we first demonstrate that technical parameters related
               to image acquisition and processing influence AI models trained
               to predict patient race, where these results partly reflect
               underlying biases in the original clinical datasets. We then
               find that mitigating the observed differences through a
               demographics-independent calibration strategy reduces the
               previously identified bias. While many factors likely contribute
               to AI bias and demographics prediction, these results highlight
               the importance of carefully considering data acquisition and
               processing parameters in AI development and healthcare equity
               more broadly.",
  journal   = "Nat. Commun.",
  publisher = "Springer Science and Business Media LLC",
  volume    =  15,
  number    =  1,
  pages     = "7465",
  month     =  aug,
  year      =  2024,
  copyright = "https://creativecommons.org/licenses/by-nc-nd/4.0",
  language  = "en"
}


@InProceedings{wu2022fairpruneachievingfairnesspruning,
author="Wu, Yawen
and Zeng, Dewen
and Xu, Xiaowei
and Shi, Yiyu
and Hu, Jingtong",
title="FairPrune: Achieving Fairness Through Pruning for Dermatological Disease Diagnosis",
booktitle="Medical Image Computing and Computer Assisted Intervention -- MICCAI 2022",
year="2022",
publisher="Springer Nature Switzerland",
address="Cham",
pages="743--753",
abstract="Many works have shown that deep learning-based medical image classification models can exhibit bias toward certain demographic attributes like race, gender, and age. Existing bias mitigation methods primarily focus on learning debiased models, which may not necessarily guarantee all sensitive information can be removed and usually comes with considerable accuracy degradation on both privileged and unprivileged groups. To tackle this issue, we propose a method, FairPrune, that achieves fairness by pruning. Conventionally, pruning is used to reduce the model size for efficient inference. However, we show that pruning can also be a powerful tool to achieve fairness. Our observation is that during pruning, each parameter in the model has different importance for different groups' accuracy. By pruning the parameters based on this importance difference, we can reduce the accuracy difference between the privileged group and the unprivileged group to improve fairness without a large accuracy drop. To this end, we use the second derivative of the parameters of a pre-trained model to quantify the importance of each parameter with respect to the model accuracy for each group. Experiments on two skin lesion diagnosis datasets over multiple sensitive attributes demonstrate that our method can greatly improve fairness while keeping the average accuracy of both groups as high as possible.",
isbn="978-3-031-16431-6"
}



@article{BENCEVIC2024108044,
title = {Understanding skin color bias in deep learning-based skin lesion segmentation},
journal = {Computer Methods and Programs in Biomedicine},
volume = {245},
pages = {108044},
year = {2024},
issn = {0169-2607},
doi = {https://doi.org/10.1016/j.cmpb.2024.108044},
url = {https://www.sciencedirect.com/science/article/pii/S0169260724000403},
author = {Marin Benčević and Marija Habijan and Irena Galić and Danilo Babin and Aleksandra Pižurica},
keywords = {AI fairness, Dermatological image analysis, Deep neural networks, Skin lesion segmentation},
}

@article{FengRadial,
author = {Feng, Li},
year = {2022},
month = {04},
pages = {},
title = {Golden‐Angle Radial MRI: Basics, Advances, and Applications},
volume = {56},
journal = {Journal of Magnetic Resonance Imaging},
doi = {10.1002/jmri.28187}
}

@inproceedings{tianfairseg,
  title={FairSeg: A Large-Scale Medical Image Segmentation Dataset for Fairness Learning Using Segment Anything Model with Fair Error-Bound Scaling},
  year={2024},
  author={Tian, Yu and Shi, Min and Luo, Yan and Kouhana, Ava and Elze, Tobias and Wang, Mengyu},
  booktitle={The Twelfth International Conference on Learning Representations}
}

@article{SiddiquiFairSeg,
author = {Siddiqui, Ismaeel and Littlefield, Nickolas and Carlson, Luke and Gong, Matthew and Chhabra, Avani and Menezes, Zoe and Mastorakos, George and Thakar, Sakshi and Abedian, Mehrnaz and Lohse, Ines and Weiss, Kurt and Plate, Johannes and Moradi, Hamidreza and Amirian, Soheyla and P. Tafti, Ahmad},
year = {2024},
month = {07},
pages = {},
title = {Fair AI-powered orthopedic image segmentation: addressing bias and promoting equitable healthcare},
volume = {14},
journal = {Scientific Reports},
doi = {10.1038/s41598-024-66873-6}
}

@article{Gibson2023APM,
  title={A practical method to simulate realistic reduced-exposure CT images by the addition of computationally generated noise.},
  author={Nicholas Mark Gibson and Amy Lee and Martin Bencsik},
  journal={Radiological physics and technology},
  year={2023},
  url={https://api.semanticscholar.org/CorpusID:265148810}
}

@inproceedings{Unet,
author={Ronneberger, Olaf
and Fischer, Philipp
and Brox, Thomas},
title={U-Net: Convolutional Networks for Biomedical Image Segmentation},
booktitle={Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2015},
year={2015},
publisher={Springer International Publishing},
address={Cham},
pages={234-241},
abstract={There is large consent that successful training of deep networks requires many thousand annotated training samples. In this paper, we present a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently. The architecture consists of a contracting path to capture context and a symmetric expanding path that enables precise localization. We show that such a network can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks. Using the same network trained on transmitted light microscopy images (phase contrast and DIC) we won the ISBI cell tracking challenge 2015 in these categories by a large margin. Moreover, the network is fast. Segmentation of a 512x512 image takes less than a second on a recent GPU. The full implementation (based on Caffe) and the trained networks are available at http://lmb.informatik.uni-freiburg.de/people/ronneber/u-net.},
isbn={978-3-319-24574-4}
}



@article{pix2pix2017,
  title={Image-to-Image Translation with Conditional Adversarial Networks},
  author={Isola, Phillip and Zhu, Jun-Yan and Zhou, Tinghui and Efros, Alexei A},
  journal={CVPR},
  year={2017}
}


@InProceedings{sde,
  title = 	 {Image Restoration with Mean-Reverting Stochastic Differential Equations},
  author =       {Luo, Ziwei and Gustafsson, Fredrik K. and Zhao, Zheng and Sj\"{o}lund, Jens and Sch\"{o}n, Thomas B.},
  booktitle = 	 {Proceedings of the 40th International Conference on Machine Learning},
  pages = 	 {23045--23066},
  year = 	 {2023},
  volume = 	 {202},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {23--29 Jul},
  publisher =    {PMLR},
  pdf = 	 {https://proceedings.mlr.press/v202/luo23b/luo23b.pdf},
  url = 	 {https://proceedings.mlr.press/v202/luo23b.html},
  abstract = 	 {This paper presents a stochastic differential equation (SDE) approach for general-purpose image restoration. The key construction consists in a mean-reverting SDE that transforms a high-quality image into a degraded counterpart as a mean state with fixed Gaussian noise. Then, by simulating the corresponding reverse-time SDE, we are able to restore the origin of the low-quality image without relying on any task-specific prior knowledge. Crucially, the proposed mean-reverting SDE has a closed-form solution, allowing us to compute the ground truth time-dependent score and learn it with a neural network. Moreover, we propose a maximum likelihood objective to learn an optimal reverse trajectory that stabilizes the training and improves the restoration results. The experiments show that our proposed method achieves highly competitive performance in quantitative comparisons on image deraining, deblurring, and denoising, setting a new state-of-the-art on two deraining datasets. Finally, the general applicability of our approach is further demonstrated via qualitative results on image super-resolution, inpainting, and dehazing. Code is available at https://github.com/Algolzw/image-restoration-sde.}
}

@inproceedings{CheXpert,
author = {Irvin, Jeremy and Rajpurkar, Pranav and Ko, Michael and Yu, Yifan and Ciurea-Ilcus, Silviana and Chute, Chris and Marklund, Henrik and Haghgoo, Behzad and Ball, Robyn and Shpanskaya, Katie and Seekins, Jayne and Mong, David A. and Halabi, Safwan S. and Sandberg, Jesse K. and Jones, Ricky and Larson, David B. and Langlotz, Curtis P. and Patel, Bhavik N. and Lungren, Matthew P. and Ng, Andrew Y.},
title = {CheXpert: a large chest radiograph dataset with uncertainty labels and expert comparison},
year = {2019},
isbn = {978-1-57735-809-1},
publisher = {AAAI Press},
url = {https://doi.org/10.1609/aaai.v33i01.3301590},
doi = {10.1609/aaai.v33i01.3301590},
booktitle = {Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence},
articleno = {73},
numpages = {8},
location = {Honolulu, Hawaii, USA},
series = {AAAI'19}
}

@article{Calabrese_2022,
   title={The University of California San Francisco Preoperative Diffuse                     Glioma MRI Dataset},
   volume={4},
   ISSN={2638-6100},
   url={http://dx.doi.org/10.1148/ryai.220058},
   DOI={10.1148/ryai.220058},
   number={6},
   journal={Radiology: Artificial Intelligence},
   publisher={Radiological Society of North America (RSNA)},
   author={Calabrese, Evan and Villanueva-Meyer, Javier E. and Rudie, Jeffrey D. and Rauschecker, Andreas M. and Baid, Ujjwal and Bakas, Spyridon and Cha, Soonmee and Mongan, John T. and Hess, Christopher P.},
   year={2022},
   month=nov }


@inproceedings{lpips,
  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}
}

@inproceedings{Cohen2021TorchXRayVisionAL,
  title={TorchXRayVision: A library of chest X-ray datasets and models},
  author={Joseph Paul Cohen and Joseph D. Viviano and Paul Bertin and Paul Morrison and Parsa Torabian and Matteo Guarrera and Matthew P. Lungren and Akshay Chaudhari and Rupert Brooks and Mohammad Hashir and Hadrien Bertrand},
  booktitle={International Conference on Medical Imaging with Deep Learning},
  year={2021},
  url={https://api.semanticscholar.org/CorpusID:240353861}
}

@inproceedings{hardt2016equalityopportunitysupervisedlearning,
author = {Hardt, Moritz and Price, Eric and Srebro, Nathan},
title = {Equality of opportunity in supervised learning},
year = {2016},
isbn = {9781510838819},
publisher = {Curran Associates Inc.},
address = {Red Hook, NY, USA},
abstract = {We propose a criterion for discrimination against a specified sensitive attribute in supervised learning, where the goal is to predict some target based on available features. Assuming data about the predictor, target, and membership in the protected group are available, we show how to optimally adjust any learned predictor so as to remove discrimination according to our definition. Our framework also improves incentives by shifting the cost of poor classification from disadvantaged groups to the decision maker, who can respond by improving the classification accuracy. We enourage readers to consult the more complete manuscript on the arXiv.},
booktitle = {Proceedings of the 30th International Conference on Neural Information Processing Systems},
pages = {3323–3331},
numpages = {9},
location = {Barcelona, Spain},
series = {NIPS'16}
}

@article{LAINE2021106077,
title = {Imaging in focus: An introduction to denoising bioimages in the era of deep learning},
journal = {The International Journal of Biochemistry and Cell Biology},
volume = {140},
pages = {106077},
year = {2021},
issn = {1357-2725},
doi = {https://doi.org/10.1016/j.biocel.2021.106077},
url = {https://www.sciencedirect.com/science/article/pii/S1357272521001588},
author = {Romain F. Laine and Guillaume Jacquemet and Alexander Krull},
keywords = {Deep learning, Denoising, Live-cell imaging, Noise, Microscopy},
abstract = {Fluorescence microscopy enables the direct observation of previously hidden dynamic processes of life, allowing profound insights into mechanisms of health and disease. However, imaging of live samples is fundamentally limited by the toxicity of the illuminating light and images are often acquired using low light conditions. As a consequence, images can become very noisy which severely complicates their interpretation. In recent years, deep learning (DL) has emerged as a very successful approach to remove this noise while retaining the useful signal. Unlike classical algorithms which use well-defined mathematical functions to remove noise, DL methods learn to denoise from example data, providing a powerful content-aware approach. In this review, we first describe the different types of noise that typically corrupt fluorescence microscopy images and introduce the denoising task. We then present the main DL-based denoising methods and their relative advantages and disadvantages. We aim to provide insights into how DL-based denoising methods operate and help users choose the most appropriate tools for their applications.}
}

@inproceedings{DRO,
  added-at = {2021-02-14T23:47:47.000+0100},
  author = {Sagawa, Shiori and Koh, Pang Wei and Hashimoto, Tatsunori B and Liang, Percy},
  biburl = {https://www.bibsonomy.org/bibtex/2b5300c935eeeb991a48a0d1bdf0e7d1c/becker},
  booktitle = {International Conference on Learning Representations (ICLR)},
  interhash = {39cff78e095b34b5b74ac8d46dfe983c},
  intrahash = {b5300c935eeeb991a48a0d1bdf0e7d1c},
  keywords = {classification distribution known network neural p21 project:bmbf relatedwork subgroup subgroups},
  timestamp = {2021-07-25T21:45:46.000+0200},
  title = {Distributionally robust neural networks for group shifts: On the importance of regularization for worst-case generalization},
  year = 2020
}


@InProceedings{JTT,
  title = 	 {Just Train Twice: Improving Group Robustness without Training Group Information},
  author =       {Liu, Evan Z and Haghgoo, Behzad and Chen, Annie S and Raghunathan, Aditi and Koh, Pang Wei and Sagawa, Shiori and Liang, Percy and Finn, Chelsea},
  booktitle = 	 {Proceedings of the 38th International Conference on Machine Learning},
  pages = 	 {6781--6792},
  year = 	 {2021},
  volume = 	 {139},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {18--24 Jul},
  publisher =    {PMLR},
  pdf = 	 {http://proceedings.mlr.press/v139/liu21f/liu21f.pdf},
  url = 	 {https://proceedings.mlr.press/v139/liu21f.html},
  abstract = 	 {Standard training via empirical risk minimization (ERM) can produce models that achieve low error on average but high error on minority groups, especially in the presence of spurious correlations between the input and label. Prior approaches to this problem, like group distributionally robust optimization (group DRO), generally require group annotations for every training point. On the other hand, approaches that do not use group annotations generally do not improve minority performance. For example, we find that joint DRO, which dynamically upweights examples with high training loss, tends to optimize for examples that are irrelevant to the specific groups we seek to do well on. In this paper, we propose a simple two-stage approach, JTT, that achieves comparable performance to group DRO while only requiring group annotations on a significantly smaller validation set. JTT first attempts to identify informative training examples, which are often minority examples, by training an initial ERM classifier and selecting the examples with high training loss. Then, it trains a final classifier by upsampling the selected examples. Crucially, unlike joint DRO, JTT does not iteratively upsample examples that have high loss under the final classifier. On four image classification and natural language processing tasks with spurious correlations, we show that JTT closes 85% of the gap in accuracy on the worst group between ERM and group DRO.}
}


@inproceedings{Creager2019FlexiblyFR,
  title={Flexibly Fair Representation Learning by Disentanglement},
  author={Elliot Creager and David Madras and J{\"o}rn-Henrik Jacobsen and Marissa A. Weis and Kevin Swersky and Toniann Pitassi and Richard S. Zemel},
  booktitle={International Conference on Machine Learning},
  year={2019},
  url={https://api.semanticscholar.org/CorpusID:174800294}
}

@inproceedings{Sarhan,
author = {Sarhan, Mhd Hasan and Navab, Nassir and Eslami, Abouzar and Albarqouni, Shadi},
title = {Fairness by Learning Orthogonal Disentangled Representations},
year = {2020},
isbn = {978-3-030-58525-9},
publisher = {Springer-Verlag},
address = {Berlin, Heidelberg},
url = {https://doi.org/10.1007/978-3-030-58526-6_44},
doi = {10.1007/978-3-030-58526-6_44},
abstract = {Learning discriminative powerful representations is a crucial step for machine learning systems. Introducing invariance against arbitrary nuisance or sensitive attributes while performing well on specific tasks is an important problem in representation learning. This is mostly approached by purging the sensitive information from learned representations. In this paper, we propose a novel disentanglement approach to invariant representation problem. We disentangle the meaningful and sensitive representations by enforcing orthogonality constraints as a proxy for independence. We explicitly enforce the meaningful representation to be agnostic to sensitive information by entropy maximization. The proposed approach is evaluated on five publicly available datasets and compared with state of the art methods for learning fairness and invariance achieving the state of the art performance on three datasets and comparable performance on the rest. Further, we perform an ablative study to evaluate the effect of each component.},
booktitle = {Computer Vision – ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX},
pages = {746–761},
numpages = {16},
keywords = {Representation learning, Disentangled representation, Fairness in machine learning},
location = {Glasgow, United Kingdom}
}

@article{Gong,
  title={Mitigating Face Recognition Bias via Group Adaptive Classifier},
  author={Sixue Gong and Xiaoming Liu and Anil K. Jain},
  journal={2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2020},
  pages={3413-3423},
  url={https://api.semanticscholar.org/CorpusID:219687431}
}

@inproceedings{Zhang,
author = {Zhang, Brian Hu and Lemoine, Blake and Mitchell, Margaret},
title = {Mitigating Unwanted Biases with Adversarial Learning},
year = {2018},
isbn = {9781450360128},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3278721.3278779},
doi = {10.1145/3278721.3278779},
abstract = {Machine learning is a tool for building models that accurately represent input training data. When undesired biases concerning demographic groups are in the training data, well-trained models will reflect those biases. We present a framework for mitigating such biases by including a variable for the group of interest and simultaneously learning a predictor and an adversary. The input to the network X, here text or census data, produces a prediction Y, such as an analogy completion or income bracket, while the adversary tries to model a protected variable Z, here gender or zip code. The objective is to maximize the predictor's ability to predict Y while minimizing the adversary's ability to predict Z. Applied to analogy completion, this method results in accurate predictions that exhibit less evidence of stereotyping Z. When applied to a classification task using the UCI Adult (Census) Dataset, it results in a predictive model that does not lose much accuracy while achieving very close to equality of odds (Hardt, et al., 2016). The method is flexible and applicable to multiple definitions of fairness as well as a wide range of gradient-based learning models, including both regression and classification tasks.},
booktitle = {Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society},
pages = {335–340},
numpages = {6},
keywords = {unbiasing, multi-task learning, debiasing, adversarial learning},
location = {New Orleans, LA, USA},
series = {AIES '18}
}


@InProceedings{KimKimKimKimKim,
author = {Kim, Byungju and Kim, Hyunwoo and Kim, Kyungsu and Kim, Sungjin and Kim, Junmo},
title = {Learning Not to Learn: Training Deep Neural Networks With Biased Data},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}

@INPROCEEDINGS {Wang,
author = { Wang, Zeyu and Qinami, Klint and Karakozis, Ioannis Christos and Genova, Kyle and Nair, Prem and Hata, Kenji and Russakovsky, Olga },
booktitle = { 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) },
title = {{ Towards Fairness in Visual Recognition: Effective Strategies for Bias Mitigation }},
year = {2020},
volume = {},
ISSN = {},
pages = {8916-8925},
abstract = { Computer vision models learn to perform a task by capturing relevant statistics from training data. It has been shown that models learn spurious age, gender, and race correlations when trained for seemingly unrelated tasks like activity recognition or image captioning. Various mitigation techniques have been presented to prevent models from utilizing or learning such biases. However, there has been little systematic comparison between these techniques. We design a simple but surprisingly effective visual recognition benchmark for studying bias mitigation. Using this benchmark, we provide a thorough analysis of a wide range of techniques. We highlight the shortcomings of popular adversarial training approaches for bias mitigation, propose a simple but similarly effective alternative to the inference-time Reducing Bias Amplification method of Zhao et al., and design a domain-independent training technique that outperforms all other methods. Finally, we validate our findings on the attribute classification task in the CelebA dataset, where attribute presence is known to be correlated with the gender of people in the image, and demonstrate that the proposed technique is effective at mitigating real-world gender bias. },
keywords = {Training;Task analysis;Image color analysis;Benchmark testing;Gray-scale;Correlation;Data models},
doi = {10.1109/CVPR42600.2020.00894},
url = {https://doi.ieeecomputersociety.org/10.1109/CVPR42600.2020.00894},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
month =Jun}


@inproceedings{
dooley2023rethinking,
title={Rethinking Bias Mitigation: Fairer Architectures Make for Fairer Face Recognition},
author={Samuel Dooley and Rhea Sanjay Sukthanker and John P Dickerson and Colin White and Frank Hutter and Micah Goldblum},
booktitle={Thirty-seventh Conference on Neural Information Processing Systems},
year={2023},
url={https://openreview.net/forum?id=1vzF4zWQ1E}
}

@INPROCEEDINGS{repair,
  author={Li, Yi and Vasconcelos, Nuno},
  booktitle={2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, 
  title={REPAIR: Removing Representation Bias by Dataset Resampling}, 
  year={2019},
  volume={},
  number={},
  pages={9564-9573},
  keywords={Datasets and Evaluation;Action Recognition ; Deep Learning ; Representation Learning; Video Analytics},
  doi={10.1109/CVPR.2019.00980}}


@inproceedings{WenlongOrtho,
author = {Deng, Wenlong and Zhong, Yuan and Dou, Qi and Li, Xiaoxiao},
title = {On Fairness of Medical Image Classification with Multiple Sensitive Attributes via Learning Orthogonal Representations},
year = {2023},
isbn = {978-3-031-34047-5},
publisher = {Springer-Verlag},
address = {Berlin, Heidelberg},
url = {https://doi.org/10.1007/978-3-031-34048-2_13},
doi = {10.1007/978-3-031-34048-2_13},
abstract = {Mitigating the discrimination of machine learning models has gained increasing attention in medical image analysis. However, rare works focus on fair treatments for patients with multiple sensitive demographic attributes, which is a crucial yet challenging problem for real-world clinical applications. In this paper, we propose a novel method for fair representation learning with respect to multi-sensitive attributes. We pursue the independence between target and multi-sensitive representations by achieving orthogonality in the representation space. Concretely, we enforce the column space orthogonality by keeping target information on the complement of a low-rank sensitive space. Furthermore, in the row space, we encourage feature dimensions between target and sensitive representations to be orthogonal. The effectiveness of the proposed method is demonstrated with extensive experiments on the CheXpert dataset. To our best knowledge, this is the first work to mitigate unfairness with respect to multiple sensitive attributes in the field of medical imaging. The code is available at .},
booktitle = {Information Processing in Medical Imaging: 28th International Conference, IPMI 2023, San Carlos de Bariloche, Argentina, June 18–23, 2023, Proceedings},
pages = {158–169},
numpages = {12},
location = {San Carlos de Bariloche, Argentina}
}

@InProceedings{FairDisCo,
author={Du, Siyi
and Hers, Ben
and Bayasi, Nourhan
and Hamarneh, Ghassan
and Garbi, Rafeef},
title={FairDisCo: Fairer AI in Dermatology via Disentanglement Contrastive Learning},
booktitle={Computer Vision -- ECCV 2022 Workshops},
year={2023},
publisher={Springer Nature Switzerland},
address={Cham},
pages={185-202},
abstract={Deep learning models have achieved great success in automating skin lesion diagnosis. However, the ethnic disparity in these models' predictions, where lesions on darker skin types are usually underrepresented and have lower diagnosis accuracy, receives little attention. In this paper, we propose FairDisCo, a disentanglement deep learning framework with contrastive learning that utilizes an additional network branch to remove sensitive attributes, i.e. skin-type information from representations for fairness and another contrastive branch to enhance feature extraction. We compare FairDisCo to three fairness methods, namely, resampling, reweighting, and attribute-aware, on two newly released skin lesion datasets with different skin types: Fitzpatrick17k and Diverse Dermatology Images (DDI). We adapt two fairness-based metrics DPM and EOM for our multiple classes and sensitive attributes task, highlighting the skin-type bias in skin lesion classification. Extensive experimental evaluation demonstrates the effectiveness of FairDisCo, with fairer and superior performance on skin lesion classification tasks.},
isbn={978-3-031-25069-9}
}



@article{Adeli2019RepresentationLW,
  title={Representation Learning with Statistical Independence to Mitigate Bias},
  author={Ehsan Adeli and Qingyu Zhao and Adolf Pfefferbaum and Edith V. Sullivan and Li Fei-Fei and Juan Carlos Niebles and Kilian M. Pohl},
  journal={2021 IEEE Winter Conference on Applications of Computer Vision (WACV)},
  year={2019},
  pages={2512-2522},
  url={https://api.semanticscholar.org/CorpusID:211069024}
}

@Article{Singh2025.03.13.25323924,
author={Singh, Rohan
and Bapna, Monika
and Diab, Abdul Rahman
and Ruiz, Emily S.
and Lotter, William},
title={How AI is used in FDA-authorized medical devices: a taxonomy across 1,016 authorizations},
journal={npj Digital Medicine},
year={2025},
month={Jul},
day={01},
volume={8},
number={1},
pages={388},
abstract={We reviewed 1016 FDA authorizations of AI/ML-enabled medical devices to develop a taxonomy capturing key variations in clinical and AI-related features. Quantitative image analysis remains the most common application, but its relative proportion has declined recently. Over 100 devices leverage AI for data generation, though none yet involve LLMs. Our taxonomy clarifies current AI usage in medical devices and provides a foundation for tracking developments as applications evolve.},
issn={2398-6352},
doi={10.1038/s41746-025-01800-1},
url={https://doi.org/10.1038/s41746-025-01800-1}
}

@inproceedings{groh2021evaluating,
  title={Evaluating deep neural networks trained on clinical images in dermatology with the fitzpatrick 17k dataset},
  author={Groh, Matthew and Harris, Caleb and Soenksen, Luis and Lau, Felix and Han, Rachel and Kim, Aerin and Koochek, Arash and Badri, Omar},
  booktitle={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition},
  pages={1820--1828},
  year={2021}
}  type    = "{Ph.D.} diss.",
  school  = "Dept.\ of Computer Science, Stanford Univ.",
  address = "Stanford, Calif.",
}

@Article{Wenderott2024,
author={Wenderott, Katharina
and Krups, Jim
and Zaruchas, Fiona
and Weigl, Matthias},
title={Effects of artificial intelligence implementation on efficiency in medical imaging---a systematic literature review and meta-analysis},
journal={npj Digital Medicine},
year={2024},
month={Sep},
day={30},
volume={7},
number={1},
pages={265},
abstract={In healthcare, integration of artificial intelligence (AI) holds strong promise for facilitating clinicians' work, especially in clinical imaging. We aimed to assess the impact of AI implementation for medical imaging on efficiency in real-world clinical workflows and conducted a systematic review searching six medical databases. Two reviewers double-screened all records. Eligible records were evaluated for methodological quality. The outcomes of interest were workflow adaptation due to AI implementation, changes in time for tasks, and clinician workload. After screening 13,756 records, we identified 48 original studies to be incuded in the review. Thirty-three studies measured time for tasks, with 67{\%} reporting reductions. Yet, three separate meta-analyses of 12 studies did not show significant effects after AI implementation. We identified five different workflows adapting to AI use. Most commonly, AI served as a secondary reader for detection tasks. Alternatively, AI was used as the primary reader for identifying positive cases, resulting in reorganizing worklists or issuing alerts. Only three studies scrutinized workload calculations based on the time saved through AI use. This systematic review and meta-analysis represents an assessment of the efficiency improvements offered by AI applications in real-world clinical imaging, predominantly revealing enhancements across the studies. However, considerable heterogeneity in available studies renders robust inferences regarding overall effectiveness in imaging tasks. Further work is needed on standardized reporting, evaluation of system integration, and real-world data collection to better understand the technological advances of AI in real-world healthcare workflows. Systematic review registration: Prospero ID CRD42022303439, International Registered Report Identifier (IRRID): RR2-10.2196/40485.},
issn={2398-6352},
doi={10.1038/s41746-024-01248-9},
url={https://doi.org/10.1038/s41746-024-01248-9}
}


@Article{biomedinformatics4020050,
AUTHOR = {Chuah, Joshua and Yan, Pingkun and Wang, Ge and Hahn, Juergen},
TITLE = {Towards the Generation of Medical Imaging Classifiers Robust to Common Perturbations},
JOURNAL = {BioMedInformatics},
VOLUME = {4},
YEAR = {2024},
NUMBER = {2},
PAGES = {889--910},
URL = {https://www.mdpi.com/2673-7426/4/2/50},
ISSN = {2673-7426},
ABSTRACT = {Background: Machine learning (ML) and artificial intelligence (AI)-based classifiers can be used to diagnose diseases from medical imaging data. However, few of the classifiers proposed in the literature translate to clinical use because of robustness concerns. Materials and methods: This study investigates how to improve the robustness of AI/ML imaging classifiers by simultaneously applying perturbations of common effects (Gaussian noise, contrast, blur, rotation, and tilt) to different amounts of training and test images. Furthermore, a comparison with classifiers trained with adversarial noise is also presented. This procedure is illustrated using two publicly available datasets, the PneumoniaMNIST dataset and the Breast Ultrasound Images dataset (BUSI dataset). Results: Classifiers trained with small amounts of perturbed training images showed similar performance on unperturbed test images compared to the classifier trained with no perturbations. Additionally, classifiers trained with perturbed data performed significantly better on test data both perturbed by a single perturbation (p-values: noise = 0.0186; contrast = 0.0420; rotation, tilt, and blur = 0.000977) and multiple perturbations (p-values: PneumoniaMNIST = 0.000977; BUSI = 0.00684) than the classifier trained with unperturbed data. Conclusions: Classifiers trained with perturbed data were found to be more robust to perturbed test data than the unperturbed classifier without exhibiting a performance decrease on unperturbed test images, indicating benefits to training with data that include some perturbed images and no significant downsides.},
DOI = {10.3390/biomedinformatics4020050}
}

@Article{Xu2024,
author={Xu, Zikang
and Li, Jun
and Yao, Qingsong
and Li, Han
and Zhao, Mingyue
and Zhou, S. Kevin},
title={Addressing fairness issues in deep learning-based medical image analysis: a systematic review},
journal={npj Digital Medicine},
year={2024},
month={Oct},
day={17},
volume={7},
number={1},
pages={286},
abstract={Deep learning algorithms have demonstrated remarkable efficacy in various medical image analysis (MedIA) applications. However, recent research highlights a performance disparity in these algorithms when applied to specific subgroups, such as exhibiting poorer predictive performance in elderly females. Addressing this fairness issue has become a collaborative effort involving AI scientists and clinicians seeking to understand its origins and develop solutions for mitigation within MedIA. In this survey, we thoroughly examine the current advancements in addressing fairness issues in MedIA, focusing on methodological approaches. We introduce the basics of group fairness and subsequently categorize studies on fair MedIA into fairness evaluation and unfairness mitigation. Detailed methods employed in these studies are presented too. Our survey concludes with a discussion of existing challenges and opportunities in establishing a fair MedIA and healthcare system. By offering this comprehensive review, we aim to foster a shared understanding of fairness among AI researchers and clinicians, enhance the development of unfairness mitigation methods, and contribute to the creation of an equitable MedIA society.},
issn={2398-6352},
doi={10.1038/s41746-024-01276-5},
url={https://doi.org/10.1038/s41746-024-01276-5}
}



@Article{Class-Seg-Importance,
author={Bian, Yueyan
and Li, Jin
and Ye, Chuyang
and Jia, Xiuqin
and Yang, Qi},
title={Artificial intelligence in medical imaging: From task-specific models to large-scale foundation models},
journal={Chinese Medical Journal},
year={2025},
volume={138},
number={6},
keywords={Medical imaging; Artificial intelligence; Task-specific model; Foundation model; Generalized representations},
abstract={Artificial intelligence (AI), particularly deep learning, has demonstrated remarkable performance in medical imaging across a variety of modalities, including X-ray, computed tomography (CT), magnetic resonance imaging (MRI), ultrasound, positron emission tomography (PET), and pathological imaging. However, most existing state-of-the-art AI techniques are task-specific and focus on a limited range of imaging modalities. Compared to these task-specific models, emerging foundation models represent a significant milestone in AI development. These models can learn generalized representations of medical images and apply them to downstream tasks through zero-shot or few-shot fine-tuning. Foundation models have the potential to address the comprehensive and multifactorial challenges encountered in clinical practice. This article reviews the clinical applications of both task-specific and foundation models, highlighting their differences, complementarities, and clinical relevance. We also examine their future research directions and potential challenges. Unlike the replacement relationship seen between deep learning and traditional machine learning, task-specific and foundation models are complementary, despite inherent differences. While foundation models primarily focus on segmentation and classification, task-specific models are integrated into nearly all medical image analyses. However, with further advancements, foundation models could be applied to other clinical scenarios. In conclusion, all indications suggest that task-specific and foundation models, especially the latter, have the potential to drive breakthroughs in medical imaging, from image processing to clinical workflows.},
issn={0366-6999},
url={https://journals.lww.com/cmj/fulltext/2025/03200/artificial_intelligence_in_medical_imaging__from.3.aspx}
}

@article{bousse2024review,
 author = {Bousse, Alexandre and Kandarpa, Venkata Sai Sundar and Shi, Kuangyu and Gong, Kuang and Lee, Jae Sung and Liu, Chi and Visvikis, Dimitris},
 doi = {10.1109/TRPMS.2023.3349194},
 journal = {IEEE Transactions on Radiation and Plasma Medical Sciences},
 publisher = {IEEE},
 title = {A Review on Low-Dose Emission Tomography Post-Reconstruction Denoising With Neural Network Approaches},
 url = {https://arxiv.org/abs/2401.00232},
 year = {2024}
}

@Article{Heckel2024,
author={Heckel, Reinhard
and Jacob, Mathews
and Chaudhari, Akshay
and Perlman, Or
and Shimron, Efrat},
title={Deep learning for accelerated and robust MRI reconstruction},
journal={Magnetic Resonance Materials in Physics, Biology and Medicine},
year={2024},
month={Jul},
day={01},
volume={37},
number={3},
pages={335-368},
abstract={Deep learning (DL) has recently emerged as a pivotal technology for enhancing magnetic resonance imaging (MRI), a critical tool in diagnostic radiology. This review paper provides a comprehensive overview of recent advances in DL for MRI reconstruction, and focuses on various DL approaches and architectures designed to improve image quality, accelerate scans, and address data-related challenges. It explores end-to-end neural networks, pre-trained and generative models, and self-supervised methods, and highlights their contributions to overcoming traditional MRI limitations. It also discusses the role of DL in optimizing acquisition protocols, enhancing robustness against distribution shifts, and tackling biases. Drawing on the extensive literature and practical insights, it outlines current successes, limitations, and future directions for leveraging DL in MRI reconstruction, while emphasizing the potential of DL to significantly impact clinical imaging practices.},
issn={1352-8661},
doi={10.1007/s10334-024-01173-8},
url={https://doi.org/10.1007/s10334-024-01173-8}
}

@article{AHISHAKIYE2021118,
title = {A survey on deep learning in medical image reconstruction},
journal = {Intelligent Medicine},
volume = {1},
number = {3},
pages = {118-127},
year = {2021},
issn = {2667-1026},
doi = {https://doi.org/10.1016/j.imed.2021.03.003},
url = {https://www.sciencedirect.com/science/article/pii/S2667102621000061},
author = {Emmanuel Ahishakiye and Martin Bastiaan {Van Gijzen} and Julius Tumwiine and Ruth Wario and Johnes Obungoloch},
keywords = {Deep learning, Open science, Image reconstruction, Medical imaging, Machine Learning},
abstract = {Medical image reconstruction aims to acquire high-quality medical images for clinical usage at minimal cost and risk to the patients. Deep learning and its applications in medical imaging, especially in image reconstruction have received considerable attention in the literature in recent years. This study reviews records obtained electronically through the leading scientific databases (Magnetic Resonance Imaging journal, Google Scholar, Scopus, Science Direct, Elsevier, and from other journal publications) searched using three sets of keywords: (1) Deep learning, image reconstruction, medical imaging; (2) Medical imaging, Deep learning, Image reconstruction; (3) Open science, Open imaging data, Open software. The articles reviewed revealed that deep learning-based reconstruction methods improve the quality of reconstructed images qualitatively and quantitatively. However, deep learning techniques are generally computationally expensive, require large amounts of training datasets, lack decent theory to explain why the algorithms work, and have issues of generalization and robustness. The challenge of lack of enough training datasets is currently being addressed by using transfer learning techniques.}
}

@article{Kingma2014AdamAM,
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}

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