用生成模型提升罕见病理切片的异常检测能力
LoGex: Improved tail detection of extremely rare histopathology classes via guided diffusion
- 结合低秩适配与扩散引导，生成针对性合成数据增强罕见类检测
- 每类仅10张样本下，罕见病检测性能显著提升，主类分类精度不变
- 适合关注医疗罕见病检测与小样本生成的科研人员
在真实医疗场景中，数据分布通常呈现长尾特征，多数样本集中于少数类别，而尾部罕见类别样本极少，往往仅有几例。这类分布带来巨大挑战：罕见病症需精准识别，却因数据稀缺难以分类。本文不尝试直接分类罕见类别，而是聚焦于将其可靠地作为分布外（OOD）数据检测。我们采用低秩适配（LoRA）与扩散引导机制，生成针对检测任务的合成数据。在仅每尾部类别提供10个样本的情况下，显著提升了在复杂病理任务上的OOD检测性能，同时保持对头部类别的分类准确率。
原文摘要 · Abstract (English)
In realistic medical settings, the data are often inherently long-tailed, with most samples concentrated in a few classes and a long tail of rare classes, usually containing just a few samples. This distribution presents a significant challenge because rare conditions are critical to detect and difficult to classify due to limited data. In this paper, rather than attempting to classify rare classes, we aim to detect these as out-of-distribution data reliably. We leverage low-rank adaption (LoRA) and diffusion guidance to generate targeted synthetic data for the detection problem. We significantly improve the OOD detection performance on a challenging histopathological task with only ten samples per tail class without losing classification accuracy on the head classes.
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