# DuetMoE: Coupling Inter- and Intra-Subgroup Robustness for Fair Medical Image Analysis

arXiv: https://arxiv.org/abs/2605.10521 · HTML: https://mlsanity.com/paper/2605.10521/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 27 references that are in the map.
- First posted on arXiv 11 May 2026.

## Builds on (most cited first, 17 of 17)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [The Power of Scale for Parameter-Efficient Prompt Tuning](https://mlsanity.com/paper/2104.08691/) (2021)
- [Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer](https://mlsanity.com/paper/1701.06538/) (2017)
- [TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation](https://mlsanity.com/paper/2102.04306/) (2021)
- [3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation](https://mlsanity.com/paper/1606.06650/) (2016)
- [Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-Case Generalization](https://mlsanity.com/paper/1911.08731/) (2019)
- [Learning Adversarially Fair and Transferable Representations](https://mlsanity.com/paper/1802.06309/) (2018)
- [Hidden Stratification Causes Clinically Meaningful Failures in Machine Learning for Medical Imaging](https://mlsanity.com/paper/1909.12475/) (2019)
- [On Tilted Losses in Machine Learning: Theory and Applications](https://mlsanity.com/paper/2109.06141/) (2021)
- [Adaptive Sampling for Stochastic Risk-Averse Learning](https://mlsanity.com/paper/1910.12511/) (2019)
- [LLM-driven Multimodal Target Volume Contouring in Radiation Oncology](https://mlsanity.com/paper/2311.01908/) (2023)
- [FairDomain: Achieving Fairness in Cross-Domain Medical Image Segmentation and Classification](https://mlsanity.com/paper/2407.08813/) (2024)
- [FairDiff: Fair Segmentation with Point-Image Diffusion](https://mlsanity.com/paper/2407.06250/) (2024)
- [Group Distributionally Robust Optimization-Driven Reinforcement Learning for LLM Reasoning](https://mlsanity.com/paper/2601.19280/) (2026)
- [Mitigating Spurious Correlation via Distributionally Robust Learning with Hierarchical Ambiguity Sets](https://mlsanity.com/paper/2510.02818/) (2025)
- [Mixture of Multicenter Experts in Multimodal AI for Debiased Radiotherapy Target Delineation](https://mlsanity.com/paper/2410.00046/) (2024)
- [Distribution-aware Fairness Learning in Medical Image Segmentation From A Control-Theoretic Perspective](https://mlsanity.com/paper/2502.00619/) (2025)

## Abstract (arXiv)

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: inter-subgroup fairness, which reduces performance disparities across groups, and intra-subgroup robustness, which protects poorly served patients within each group. To jointly address these objectives, we propose DuetMoE, a subgroup-aware mixture-of-experts framework that couples group-level adaptation with patient-specific clinical guidance, enabling more reliable medical image analysis for individual patients. For settings without linked clinical records, we further introduce DuetMoE+, which retains subgroup-routed experts and incorporates a KL-constrained distributionally robust objective to address intra-subgroup robustness. We evaluate our methods on PI-CAI, radiotherapy, and Harvard-FairSeg. Across four evaluation settings, our methods lead in overall or equity-scaled performance, reduce the inter-subgroup mean Dice gap by up to 30.7%, and raise intra-subgroup 25th-percentile Dice by up to 11.5 points over the strongest reported baselines.