# Towards Fair and Robust Volumetric CT Classification via KL-Regularised Group Distributionally Robust Optimisation

arXiv: https://arxiv.org/abs/2603.15941 · HTML: https://mlsanity.com/paper/2603.15941/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 30 references that are in the map.
- First posted on arXiv 16 Mar 2026.

## Builds on (most cited first, 15 of 15)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-Case Generalization](https://mlsanity.com/paper/1911.08731/) (2019)
- [MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer](https://mlsanity.com/paper/2110.02178/) (2021)
- [Domain Adaptation for Medical Image Analysis: A Survey](https://mlsanity.com/paper/2102.09508/) (2021)
- [An Investigation of Why Overparameterization Exacerbates Spurious Correlations](https://mlsanity.com/paper/2005.04345/) (2020)
- [Deep Transparent Prediction through Latent Representation Analysis](https://mlsanity.com/paper/2009.07044/) (2020)
- [MIA-COV19D: COVID-19 Detection through 3-D Chest CT Image Analysis](https://mlsanity.com/paper/2106.07524/) (2021)
- [Domain Generalization for Medical Image Analysis: A Review](https://mlsanity.com/paper/2310.08598/) (2023)
- [A Deep Neural Architecture for Harmonizing 3-D Input Data Analysis and Decision Making in Medical Imaging](https://mlsanity.com/paper/2303.00175/) (2023)
- [COVID-19 Computer-aided Diagnosis through AI-assisted CT Imaging Analysis: Deploying a Medical AI System](https://mlsanity.com/paper/2403.06242/) (2024)
- [SAM2CLIP2SAM: Vision Language Model for Segmentation of 3D CT Scans for Covid-19 Detection](https://mlsanity.com/paper/2407.15728/) (2024)
- [Multi-Source COVID-19 Detection via Variance Risk Extrapolation](https://mlsanity.com/paper/2506.23208/) (2025)
- [SwIPE: Efficient and Robust Medical Image Segmentation with Implicit Patch Embeddings](https://mlsanity.com/paper/2307.12429/) (2023)
- [Advancing Lung Disease Diagnosis in 3D CT Scans](https://mlsanity.com/paper/2507.00993/) (2025)
- [Multi Source COVID-19 Detection via Kernel-Density-based Slice Sampling](https://mlsanity.com/paper/2507.01564/) (2025)

## Abstract (arXiv)

Automated diagnosis from chest computed tomography (CT) scans faces two persistent challenges in clinical deployment: distribution shift across acquisition sites and performance disparity across demographic subgroups. We address both simultaneously across two complementary tasks: binary COVID-19 classification from multi-site CT volumes (Task 1) and four-class lung pathology recognition with gender-based fairness constraints (Task 2). Our framework combines a lightweight MobileViT-XXS slice encoder with a two-layer SliceTransformer aggregator for volumetric reasoning, and trains with a KL-regularised Group Distributionally Robust Optimisation (Group DRO) objective that adaptively upweights underperforming acquisition centres and demographic subgroups. Unlike standard Group DRO, the KL penalty prevents group weight collapse, providing a stable balance between worst-case protection and average performance. For Task 2, we define groups at the granularity of gender class, directly targeting severely underrepresented combinations such as female Squamous cell carcinoma. On Task 1, our best configuration achieves a challenge F1 of 0.835, surpassing the best published challenge entry by +5.9. On Task 2, Group DRO with α = 0.5 achieves a mean per-gender macro F1 of 0.815, outperforming the best challenge entry by +11.1 pp and improving Female Squamous F1 by +17.4 over the Focal Loss baseline.