DuetMoE: קישור בין צדקנות בין-ובתוך-קבוצות לניתוח תמונות רפואי

תקציר מקורי באנגליתarXiv:2605.10521v2 Announce Type: replace-cross Abstract: As medical AI expands across diverse healthcare settings worldwide, equitable performance across patient populations is becoming essential to trustworthy clinical use. Fairness in medical image analysis is often evaluated through average performance across predefined subgroups, yet similar subgroup averages can conceal substantial variation among individual patients. Therefore, a reliable medical AI requires addressing two complementary objectives: \emph{inter-subgroup fairness}, which reduces performance disparities across groups, and \emph{intra-subgroup robustness}, which protects poorly served patients within each group. To jointly address these objectives, we propose \textbf{DuetMoE}, a subgroup-aware mixture-of-experts framewo