通过观察分组提升胸部X光片疾病分类的因果表示学习
Causal Representation Learning with Observational Grouping for CXR Classification
- 基于观测分组构建可识别因果表示，增强模型鲁棒性
- 在种族、性别和拍摄视角上实现不变性，提升跨任务泛化能力
- 适用于医疗影像中需消除混杂因素的任务场景
可识别的因果表示学习旨在揭示数据生成过程中的真实因果关系。在医学影像领域，这为提升特定任务潜在特征的泛化能力和鲁棒性提供了机遇。本文提出一种基于观测分组的端到端框架，用于胸部X光片疾病分类中的可识别表示学习。实验表明，通过分组强制实现种族、性别和成像视角上的不变性，该方法显著提升了多个分类任务的泛化性能与鲁棒性。
原文摘要 · Abstract (English)
Identifiable causal representation learning seeks to uncover the true causal relationships underlying a data generation process. In medical imaging, this presents opportunities to improve the generalisability and robustness of task-specific latent features. This work introduces the concept of grouping observations to learn identifiable representations for disease classification in chest X-rays via an end-to-end framework. Our experiments demonstrate that these causal representations improve generalisability and robustness across multiple classification tasks when grouping is used to enforce invariance w.r.t race, sex, and imaging views.
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