[2511.08887] FAST-CAD: A Fairness-Aware Framework for Non-Contact Stroke Diagnosis

Computer Science > Machine Learning arXiv:2511.08887 (cs) [Submitted on 12 Nov 2025 (v1), last revised 5 Apr 2026 (this version, v4)] Title:FAST-CAD: A Fairness-Aware Framework for Non-Contact Stroke Diagnosis Authors:Tommy Sha, Zhan Cheng, Haotian Zhai, Xuwei Ding, Junnan Li, Haixiang Tang, Zaoting Sun, Yanchuan Tang, Yongzhe (Kindred)Yi, Yuan Gao, Anhao Li View a PDF of the paper titled FAST-CAD: A Fairness-Aware Framework for Non-Contact Stroke Diagnosis, by Tommy Sha and 10 other authors View PDF HTML (experimental) Abstract:Stroke is an acute cerebrovascular disease, and timely diagnosis significantly improves patient survival. However, existing automated diagnosis methods suffer from fairness issues across demographic groups, potentially exacerbating healthcare disparities. In this work we propose FAST-CAD, a theoretically grounded framework that combines domain-adversarial training (DAT) with group distributionally robust optimization (Group-DRO) for fair and accurate non-contact stroke diagnosis. Our approach is built on domain adaptation and minimax fairness theory and provides convergence guarantees and fairness bounds. We curate a multimodal dataset covering 12 demographic subgroups defined by age, gender, and posture. FAST-CAD employs self-supervised encoders with adversarial domain discrimination to learn demographic-invariant representations, while Group-DRO optimizes worst-group risk to ensure robust performance across all subgroups. Extensive experiments sh...