用无监督方法生成真实病理图像，提升小样本分类效果。
USegMix: Unsupervised Segment Mix for Efficient Data Augmentation in Pathology Images
- 自动提取组织片段构建池，无需人工标注。
- 替换并融合片段后，分类准确率显著提升。
- 适合缺乏标注数据的病理图像研究者使用。
在计算病理学中，研究人员常因标注病理数据集稀缺而面临挑战。数据增强成为缓解这一问题的关键技术。本文提出一种高效的病理图像数据增强方法USegMix。该方法分两阶段生成合成图像：第一阶段，利用超像素和分割任意模型（SAM）自动、无监督地构建组织片段池；第二阶段，从目标图像中选取候选片段，用片段池中相似片段替换，并通过预训练扩散模型进行融合。该方法可生成多样且真实的病理图像。我们在结直肠癌和前列腺癌两个病理图像数据集上严格评估了USegMix的有效性，结果表明其显著提升了癌症分类性能，验证了该方法在病理图像分析中的巨大潜力。
原文摘要 · Abstract (English)
In computational pathology, researchers often face challenges due to the scarcity of labeled pathology datasets. Data augmentation emerges as a crucial technique to mitigate this limitation. In this study, we introduce an efficient data augmentation method for pathology images, called USegMix. Given a set of pathology images, the proposed method generates a new, synthetic image in two phases. In the first phase, USegMix constructs a pool of tissue segments in an automated and unsupervised manner using superpixels and the Segment Anything Model (SAM). In the second phase, USegMix selects a candidate segment in a target image, replaces it with a similar segment from the segment pool, and blends them by using a pre-trained diffusion model. In this way, USegMix can generate diverse and realistic pathology images. We rigorously evaluate the effectiveness of USegMix on two pathology image datasets of colorectal and prostate cancers. The results demonstrate improvements in cancer classification performance, underscoring the substantial potential of USegMix for pathology image analysis.
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