Fairness of AI in Medical Imaging

.- LTCXNet: Tackling Long-Tailed Multi-Label Classification and Racial Bias in Chest X-Ray Analysis. .- Fairness and Robustness of CLIP-Based Models for Chest X-rays. .- ShortCXR: Benchmarking Self-Supervised Learning Methods for Shortcut Mitigation in Chest X-Ray Interpretation. .- How Fair Are Foundation Models? Exploring the Role of Covariate Bias in Histopathology. .- The Cervix in Context: Bias Assessment in Preterm Birth Prediction. .- Identifying Gender-Specific Visual Bias Signals in Skin Lesion Classification. .- Fairness-Aware Data Augmentation for Cardiac MRI using Text-Conditioned Diffusion Models. .- Exploring the interplay of label bias with subgroup size and separability: A case study in mammographic density classification. .- Does a Rising Tide Lift All Boats? Bias Mitigation for AI-based CMR Segmentation. .- MIMM-X: Disentangeling Spurious Correlations for Medical Image Analysis. .- Predicting Patient Self-reported Race From Skin Histological Images with Deep Learning. .- Robustness and sex differences in skin cancer detection: logistic regression vs CNNs. .- Sex-based Bias Inherent in the Dice Similarity Coefficient: A Model Independent Analysis for Multiple Anatomical Structures. .- The Impact of Skin Tone Label Granularity on the Performance and Fairness of AI Based Dermatology Image Classification Models. .- Causal Representation Learning with Observational Grouping for CXR Classification. .- Invisible Attributes, Visible Biases: Exploring Demographic Shortcuts in MRI-based Alzheimer’s Disease Classification. .- Fair Dermatological Disease Diagnosis through Auto-weighted Federated Learning and Performance-aware Personalization. .- Assessing Annotator and Clinician Biases in an Open-Source-Based Tool Used to Generate Head CT Segmentations for Deep Learning Training. .- meval: A Statistical Toolbox for Fine-Grained Model Performance Analysis. .- Revisiting the Evaluation Bias Introduced by Frame Sampling Strategies in Surgical Video Segmentation Using SAM2. .- Disentanglement and Assessment of Shortcuts in Ophthalmological Retinal Imaging Exams.