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This book constitutes the refereed proceedings of the Second International Workshop, FAIMI 2024, and the Third International Workshop, EPIMI 2024, held in conjunction with MICCAI 2024, Marrakesh, Morocco, in October 2024.
The 17 full papers presented in this book were carefully reviewed and selected from 21 submissions.
FAIMI aimed to raise awareness about potential fairness issues in machine learning within the context of biomedical image analysis.
The instance of EPIMI concentrates on topics surrounding open science, taking a critical lens on the subject.
List of contents
FAIMI: Slicing Through Bias: Explaining Performance Gaps in Medical Image Analysis using Slice Discovery Methods.- Dataset Distribution Impacts Model Fairness: Single vs Multi-Task Learning.- AI Fairness in Medical Imaging: Controlling for Disease Severity.- Fair and Private CT Contrast Agent Detection.- Mitigating Overdiagnosis Bias in CNN-Based Alzheimer's Disease Diagnosis for the Elderly.- Fair AI Outcomes Without Sacrificing Group Gains .- All you need is a guiding hand: mitigating shortcut bias in deep learning models for medical imaging.- Exploring Fairness in State-of-the-Art Pulmonary Nodule Detection Algorithms.- Quantifying the Impact of Population Shift Across Age and Sex for Abdominal Organ Segmentation.- BMFT: Achieving Fairness via Bias-based Weight Masking Fine-tuning.- Using Backbone Foundation Model for Evaluating Fairness in Chest Radiography Without Demographic Data.- Do sites benefit equally from distributed learning in medical image analysis.- Cycle-GANs generated difference maps to interpret race prediction from medical images.- On Biases in a UK Biobank-based Retinal Image Classification Model.- Investigating Gender Bias in Lymph-node Segmentation with Anatomical Priors.- EPIMI: Assessing the Impact of Sociotechnical Harms in AI-based Medical Image Analysis.- Practical and Ethical Considerations for Generative AI in Medical Imaging.
