提出新模型提升多染色肾小球分割的鲁棒性
DSA-CycleGAN: A Domain Shift Aware CycleGAN for Robust Multi-Stain Glomeruli Segmentation
- 引入领域偏移感知机制，降低染色转换中的噪声
- 在生物差异大的染色间转换时分割性能显著提升
- 适合需要跨染色训练的病理图像分割研究者
数字病理学中，不同染色及同一染色内部的变异会降低分割模型性能。标注每种染色样本耗时费力，因此采用CycleGAN进行染色转换以实现单染色标签训练多染色分割模型。然而，由于部分染色对存在一对多映射关系，传统CycleGAN在转换过程中易引入噪声，与其循环一致性损失相冲突。为此，本文提出领域偏移感知的CycleGAN（DSA-CycleGAN），有效减少此类噪声。同时评估了机器学习领域若干相关改进方法，并在多染色肾小球分割任务中对比其效果。实验表明，DSA-CycleGAN不仅提升了分割性能，且在生物差异较大的染色间转换中降噪效果更优。代码已开源。
原文摘要 · Abstract (English)
A key challenge in segmentation in digital histopathology is inter- and intra-stain variations as it reduces model performance. Labelling each stain is expensive and time-consuming so methods using stain transfer via CycleGAN, have been developed for training multi-stain segmentation models using labels from a single stain. Nevertheless, CycleGAN tends to introduce noise during translation because of the one-to-many nature of some stain pairs, which conflicts with its cycle consistency loss. To address this, we propose the Domain Shift Aware CycleGAN, which reduces the presence of such noise. Furthermore, we evaluate several advances from the field of machine learning aimed at resolving similar problems and compare their effectiveness against DSA-CycleGAN in the context of multi-stain glomeruli segmentation. Experiments demonstrate that DSA-CycleGAN not only improves segmentation performance in glomeruli segmentation but also outperforms other methods in reducing noise. This is particularly evident when translating between biologically distinct stains. The code is publicly available at https://github.com/zeeshannisar/DSA-CycleGAN.
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