RED-Sphere：用于跨人群眼底疾病领域泛化的超球面残差边缘去偏
RED-Sphere: Hyperspherical Residual Edge Debiasing for Cross-Population Fundus Disease Domain Generalization
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中文总结 AI 辅助
研究跨人群眼底疾病领域泛化问题，提出RED-Sphere框架，通过估计干扰响应、残差软门控、正则化损失及用球面原型预测标签来提升鲁棒性，在相关分类任务中取得更好效果，验证了该框架的有效性。
中文摘要 AI 辅助
医学图像分类器通常在一个源人群中训练，但临床部署需要对外观、采集方式和疾病患病率与源队列不同的患者具有鲁棒性。现有方法存在不足。本文研究严格仅源跨人群设置。提出RED-Sphere框架，通过边缘和特征能量先验估计捷径敏感干扰响应，经残差软门控衰减主导响应，用一致性和分离损失正则化屏蔽干扰视图，用归一化球面原型预测标签。在年龄相关性黄斑变性和糖尿病性视网膜病变的二维扫描激光眼底镜分类中验证有效，该原理可适用于其他领域。在严格的仅白人哈佛-公平视觉协议下，RED-Sphere在所有20项任务和骨干比较中提高了留出的宏F1，在年龄相关性黄斑变性和糖尿病性视网膜病变上平均提高了1.28和2.98个F1点。曲线下面积、精确率-召回率曲线下面积、视觉诊断、消融和敏感性分析的结果进一步支持了更强的外部语义对齐和更稳定的角度疾病几何形状。
英文摘要
Medical image classifiers are often trained within one source population, yet clinical deployment requires robustness to patients whose appearance, acquisition style, and disease prevalence differ from the source cohort. Existing fairness and robustness methods often require group supervision or treat appearance variation as an undifferentiated nuisance, which is insufficient when population-correlated low-level cues and lesion evidence share edge and texture structure. We study a strict source-only cross-population setting, where external populations are unseen during optimization, validation, scheduling, hyperparameter and model selection. We propose RED-Sphere, a plug-and-play robustness framework for image classification under unseen population shifts. It estimates shortcut-sensitive nuisance responses with an edge and feature energy prior, attenuates dominant responses through residual soft gating, regularizes masked nuisance views with counterfactual-inspired consistency and separation losses, and predicts labels with normalized spherical prototypes. It favours angular semantic evidence over source-correlated activation magnitude while preserving lesion structure. Although demonstrated on 2D Scanning Laser Ophthalmoscopy (SLO) fundus classification for Age-Related Macular Degeneration (AMD) and Diabetic Retinopathy (DR), RED-Sphere is not tied to retinal anatomy: the same principle can be adapted with modality-specific nuisance priors wherever appearance shortcuts and semantic evidence are entangled. Under a strict White-only Harvard-FairVision protocol, RED-Sphere improves held-out macro-F1 across all 20 task and backbone comparisons, with average gains of 1.28 and 2.98 F1 points on AMD and DR. Gains in AUC and PR-AUC, visual diagnostics, ablations, and sensitivity analyses further support stronger external semantic alignment and more stable angular disease geometry.
发表机构
- Newcastle University（纽卡斯尔大学）
- King Abdullah University of Science and Technology (KAUST)（阿卜杜拉国王科技大学）
- Durham University（杜伦大学）
机构由 AI 辅助整理，请以论文原文为准。
