arXiv AI
DuetMoE: Coupling Inter- and Intra-Subgroup Robustness for Fair Medical Image Analysis
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arXiv AI
Jun 10
People-Centred Medical Image Analysis via Fairness-Aware Human-AI Cooperation
arXiv:2604. 26991v2 Announce Type: replace-cross Abstract: Machine learning models for medical image analysis often exhibit subgroup-dependent performance, which impacts how decisions should be allocated between automated systems and human experts under limited resources.
arXiv AI
Jul 13
Beyond Metadata: CAPRA for Hidden Subgroup Analysis under Missing Metadata in Medical Imaging
arXiv:2607. 09102v1 Announce Type: cross Abstract: Medical imaging models are often deployed without the demographic, acquisition, and quality metadata needed for subgroup auditing.
arXiv Machine Learning
Jun 15
MOSIC: Model-Agnostic Optimal Subgroup Identification with Multi-Constraint for Improved Reliability
arXiv:2504. 20908v3 Announce Type: replace Abstract: Current subgroup identification methods typically follow a two-step approach: first estimate conditional average treatment effects and then apply thresholding or rule-based procedures to define subgroups.
arXiv Computer Vision
Aug 31
Medical Imaging AI Competitions Lack Fairness
Benchmarking competitions are central to AI development in medical imaging, but it is unclear if they provide representative, accessible, and reusable data for clinical relevance. This study systematically examined 249 challenges (458 tasks) across 19 imaging modalities, finding limited geographic, modality, and problem-type representation. Additionally, many datasets suffer from restrictive access, ambiguous licensing, and poor documentation, hindering reproducibility and long-term reuse.
arXiv AI
Sep 16
Fairness at Every Intersection: Uncovering and Mitigating Intersectional Biases in Multimodal Clinical Predictions
The paper investigates intersectional biases in multimodal clinical predictions using Electronic Healthcare Records (EHR). It introduces datasets MIMIC-Eye1 and MIMIC-IV ED, applies unified text representations from pre‑trained clinical language models, and benchmarks bias mitigation at the intersectional subgroup level. Results show that subgroup‑specific mitigation is robust across datasets, subgroups, and embeddings, effectively addressing intersectional biases in multimodal settings.
arXiv Machine Learning
Jul 31
TIER-MoE: Trust-Informed Expert Routing via Conditional Modality Risk for Multimodal Fusion in Biomedical Classification
arXiv:2607. 27289v1 Announce Type: new Abstract: The promise of multimodal fusion lies in combining complementary sources of evidence, yet more evidence does not always yield a better prediction.