A fully convolutional neural network for explainable classification of attention deficit hyperactivity disorder. - Emma A. M. Stanley; Deepthi Rajashekar; Pauline Mouches; Matthias Wilms; Kira Plettl; Nils D. Forkert (Computer-Aided Diagnosis, 2022)

Disproportionate Subgroup Impacts and Other Challenges of Fairness in Artificial Intelligence for Medical Image Analysis. - Emma A. M. Stanley; Matthias Wilms; Nils D. Forkert (EPIMI/ML-CDS@MICCAI, 2022)

On the Relationship Between Open Science in Artificial Intelligence for Medical Imaging and Global Health Equity. - Raissa Souza; Emma A. M. Stanley; Nils D. Forkert (CLIP/FAIMI/EPIMI@MICCAI, 2023)

An analysis of intensity harmonization techniques for Parkinson's multi-site MRI datasets. - Raissa Souza; Emma A. M. Stanley; Milton Camacho; Matthias Wilms; Nils D. Forkert (Computer-Aided Diagnosis, 2023)

A Flexible Framework for Simulating and Evaluating Biases in Deep Learning-Based Medical Image Analysis. - Emma A. M. Stanley; Matthias Wilms; Nils D. Forkert (MICCAI (2), 2023)

Towards objective and systematic evaluation of bias in medical imaging AI. - Emma A. M. Stanley; Raissa Souza; Anthony J. Winder; Vedant Gulve; Kimberly Amador; Matthias Wilms; Nils D. Forkert (CoRR, 2023)

Do Sites Benefit Equally from Distributed Learning in Medical Image Analysis? - Raissa Souza; Emma A. M. Stanley; Richard Camicioli; Oury Monchi; Zahinoor Ismail; Matthias Wilms; Nils D. Forkert (FAIMI/EPIMI@MICCAI, 2024)

Assessing the Impact of Sociotechnical Harms in AI-Based Medical Image Analysis. - Emma A. M. Stanley; Raissa Souza; Anthony J. Winder; Matthias Wilms; G. Bruce Pike; Gabrielle Dagasso; Christopher Nielsen; Sarah J. MacEachern; Nils D. Forkert (FAIMI/EPIMI@MICCAI, 2024)

A Lightweight 3D Conditional Diffusion Model for Self-explainable Brain Age Prediction in Adults and Children. - Matthias Wilms; Ahmad O. Ahsan; Erik Y. Ohara; Gabrielle Dagasso; Elizabeth Macavoy; Emma A. M. Stanley; Vibujithan Vigneshwaran; Nils D. Forkert (MLCN@MICCAI, 2024)

Trustworthy and Responsible AI for Human-Centric Autonomous Decision-Making Systems. - Farzaneh Dehghani; Mahsa Dibaji; Fahim Anzum; Lily Dey; Alican Basdemir; Sayeh Bayat; Jean-Christophe Boucher; Steve Drew; Sarah Elaine Eaton; Richard Frayne; Gouri Ginde; Ashley Harris; et al. (CoRR, 2024)

A multi-center distributed learning approach for Parkinson's disease classification using the traveling model paradigm. - Raissa Souza; Emma A. M. Stanley; Milton Camacho; Richard Camicioli; Oury Monchi; Zahinoor Ismail; Matthias Wilms; Nils D. Forkert (Frontiers Artif. Intell., 2024)

Towards objective and systematic evaluation of bias in artificial intelligence for medical imaging. - Emma A. M. Stanley; Raissa Souza; Anthony J. Winder; Vedant Gulve; Kimberly Amador; Matthias Wilms; Nils D. Forkert (J. Am. Medical Informatics Assoc., 2024)

Identifying Biases in a Multicenter MRI Database for Parkinson's Disease Classification: Is the Disease Classifier a Secret Site Classifier? - Raissa Souza; Anthony J. Winder; Emma A. M. Stanley; Vibujithan Vigneshwaran; Milton Camacho; Richard Camicioli; Oury Monchi; Matthias Wilms; Nils D. Forkert (IEEE J. Biomed. Health Informatics, 2024)

Self-supervised identification and elimination of harmful datasets in distributed machine learning for medical image analysis. - Raissa Souza; Emma A. M. Stanley; Anthony J. Winder; Chris Kang; Kimberly Amador; Erik Y. Ohara; Gabrielle Dagasso; Richard Camicioli; Oury Monchi; Zahinoor Ismail; Matthias Wilms; Nils D. Forkert (npj Digit. Medicine, 2025)

Exploring the interplay of label bias with subgroup size and separability: A case study in mammographic density classification. - Emma A. M. Stanley; Raghav Mehta; Mélanie Roschewitz; Nils D. Forkert; Ben Glocker (CoRR, 2025)