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ORCIDFAIMI@MICCAI 2025: Daejeon, South Korea
- Esther Puyol-Antón
, Enzo Ferrante, Aasa Feragen, Andrew P. King, Veronika Cheplygina, Melanie Ganz-Benjaminsen, Ben Glocker, Eike Petersen, Heisook Lee:
Fairness of AI in Medical Imaging - Third International Workshop, FAIMI 2025, Held in Conjunction with MICCAI 2025, Daejeon, South Korea, September 23, 2025, Proceedings. Lecture Notes in Computer Science 15976, Springer 2026, ISBN 978-3-032-05869-0 - Chin-Wei Huang, Chi-Yu Chen, Mu-Yi Shen, Kuan-Chang Shih, Shih-Chih Lin, Po-Chih Kuo:
LTCXNet: Tackling Long-Tailed Multi-label Classification and Racial Bias in Chest X-Ray Analysis. 1-10 - Théo Sourget, David S. Restrepo, Céline Hudelot, Enzo Ferrante, Stergios Christodoulidis, Maria Vakalopoulou:
Fairness and Robustness of CLIP-Based Models for Chest X-Rays. 11-21 - You-Qi Chang-Liao, Po-Chih Kuo:
ShortCXR: Benchmarking Self-supervised Learning for Shortcut Mitigation in Chest X-Ray Diagnostics. 22-31 - Abubakr Shafique, Amanda Dy, Xiaoli Qin, Najd Alshamlan, Susan Done, Dimitrios Androutsos, April Khademi:
How Fair are Foundation Models? Exploring the Role of Covariate Bias in Histopathology. 32-42 - Joris Fournel, Paraskevas Pegios, Emilie Pi Fogtmann Sejer, Martin Grønnebæk Tolsgaard, Aasa Feragen:
The Cervix in Context: Bias Assessment in Preterm Birth Prediction. 43-52 - Heejae Lee, Sejung Yang, Yuseong Chu, Byungho Oh:
Identifying Gender-Specific Visual Bias Signals in Skin Lesion Classification. 53-62 - Grzegorz Skorupko, Richard Osuala, Zuzanna Szafranowska, Kaisar Kushibar, Vien Ngoc Dang, Nay Aung, Steffen E. Petersen, Karim Lekadir, Polyxeni Gkontra:
Fairness-Aware Data Augmentation for Cardiac MRI Using Text-Conditioned Diffusion Models. 63-73 - Emma A. M. Stanley, Raghav Mehta, Mélanie Roschewitz, Nils D. Forkert, Ben Glocker:
Exploring the Interplay of Label Bias with Subgroup Size and Separability: A Case Study in Mammographic Density Classification. 74-83 - Tiarna Lee, Esther Puyol-Antón, Bram Ruijsink, Miaojing Shi, Andrew P. King:
Does a Rising Tide Lift All Boats? Bias Mitigation for AI-Based CMR Segmentation. 84-93 - Louisa Fay, Hajer Reguigui, Bin Yang, Sergios Gatidis, Thomas Küstner:
MIMM-X: Disentangling Spurious Correlations for Medical Image Analysis. 94-103 - Shengjia Chen, Ruchika Verma, Kevin Clare, Jannes Jegminat, Eugenia Alleva, Kuan-Lin Huang, Brandon Veremis, Thomas J. Fuchs, Gabriele Campanella:
Predicting Patient Self-reported Race From Skin Histological Images with Deep Learning. 104-114 - Nikolette Pedersen, Regitze Sydendal, Andreas Wulff, Ralf Raumanns, Eike Petersen, Veronika Cheplygina:
Robustness and Sex Differences in Skin Cancer Detection: Logistic Regression vs CNNs. 115-124 - Hartmut Häntze, Myrthe A. D. Buser, Alessa Hering, Lisa C. Adams, Keno K. Bressem:
Sex-Based Bias Inherent in the Dice Similarity Coefficient: A Model Independent Analysis for Multiple Anatomical Structures. 125-134 - Partha Shah, Durva Sankhe, Maariyah Rashid, Zakaa Khaled, Esther Puyol-Antón, Tiarna Lee, Maram Alqarni, Sweta Rai, Andrew P. King:
The Impact of Skin Tone Label Granularity on the Performance and Fairness of AI Based Dermatology Image Classification Models. 135-144 - Rajat Rasal, Avinash Kori, Ben Glocker:
Causal Representation Learning with Observational Grouping for CXR Classification. 145-155 - Akshit Achara, Esther Puyol-Antón, Alexander Hammers, Andrew P. King:
Invisible Attributes, Visible Biases: Exploring Demographic Shortcuts in MRI-Based Alzheimer's Disease Classification. 156-166 - Gelei Xu, Yawen Wu, Zhenge Jia, Jingtong Hu, Yiyu Shi:
Fair Dermatological Disease Diagnosis Through Auto-weighted Federated Learning and Performance-Aware Personalization. 167-176 - Artur Jose Marques Paulo, Pedro Vinicius Alves Silva, Tayran Olegário, Paula Bresciani de Andrade, Klaus Schumacher, Rafael Maffei Loureiro, Joselisa Peres Queiroz de Paiva, Raissa Souza, Bruna Garbes Goncalves Pinto:
Assessing Annotator and Clinician Biases in an Open-Source-Based Tool Used to Generate Head CT Segmentations for Deep Learning Training. 177-186 - Dishantkumar Sutariya, Eike Petersen:
meval: A Statistical Toolbox for Fine-Grained Model Performance Analysis. 187-197 - Utku Ozbulak, Seyed Amir Mousavi, Francesca Tozzi, Niki Rashidian, Wouter Willaert, Wesley De Neve, Joris Vankerschaver:
Revisiting the Evaluation Bias Introduced by Frame Sampling Strategies in Surgical Video Segmentation Using SAM2. 198-207 - Leonor Fernandes, Tiago Gonçalves, João Matos, Luis Filipe Nakayama, Jaime S. Cardoso:
Disentanglement and Assessment of Shortcuts in Ophthalmological Retinal Imaging Exams. 208-217
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