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.

翻译：胸部计算机断层扫描（CT）的自动诊断在临床部署中面临两大持续挑战：跨采集站点的分布偏移以及不同人口统计亚组间的性能差异。我们通过两个互补任务同时解决这两个问题：基于多站点CT体积的二元新冠肺炎分类（任务1）以及受性别公平性约束的四类肺部病理识别（任务2）。我们的框架结合轻量级MobileViT-XXS切片编码器与双层SliceTransformer聚合器进行体积推理，并采用KL正则化组分布鲁棒优化（Group DRO）目标进行训练，该目标自适应地提升表现不佳的采集中心和人口统计亚组的权重。与标准Group DRO不同，KL惩罚项阻止组权重坍塌，在极端情况保护与平均性能之间提供稳定平衡。对于任务2，我们以性别类别为粒度定义组，直接针对严重代表性不足的组合（如女性鳞状细胞癌）。在任务1中，我们的最佳配置达到挑战F1分数0.835，超过已发表的最佳挑战条目+5.9。在任务2中，α=0.5的Group DRO实现每性别宏平均F1为0.815，超越最佳挑战条目+11.1个百分点，并将女性鳞状细胞癌F1较Focal Loss基线提升+17.4。