Description
Book Synopsis.- 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.
