无需训练，用特征分解实现病理图像无监督分割
Segmentation by Factorization: Unsupervised Semantic Segmentation for Pathology by Factorizing Foundation Model Features
- 通过分解预训练模型的特征生成分割掩码
- 使用病理基础模型使分割质量显著提升
- 适用于无标注病理图像分析，适合医学影像研究者
我们提出分割因子化（F-SEG），一种针对病理图像的无监督分割方法，可从预训练深度学习模型中生成语义分割掩码。F-SEG无需额外训练或微调，通过将模型提取的空间特征分解为分割掩码及其对应的概念特征实现。我们在癌症基因组图谱计划（TCGA）数据集上，利用多个深度学习模型提取的特征，训练多种聚类数的聚类模型，构建通用组织表型。随后，利用现成的深度学习模型，将这些聚类用于因子化对应的分割掩码。实验表明，F-SEG对H&E病理图像具有鲁棒的无监督分割能力，且使用病理基础模型后分割质量显著提升。我们还讨论并提出了评估病理图像无监督分割性能的方法。
原文摘要 · Abstract (English)
We introduce Segmentation by Factorization (F-SEG), an unsupervised segmentation method for pathology that generates segmentation masks from pre-trained deep learning models. F-SEG allows the use of pre-trained deep neural networks, including recently developed pathology foundation models, for semantic segmentation. It achieves this without requiring additional training or finetuning, by factorizing the spatial features extracted by the models into segmentation masks and their associated concept features. We create generic tissue phenotypes for H&E images by training clustering models for multiple numbers of clusters on features extracted from several deep learning models on The Cancer Genome Atlas Program (TCGA), and then show how the clusters can be used for factorizing corresponding segmentation masks using off-the-shelf deep learning models. Our results show that F-SEG provides robust unsupervised segmentation capabilities for H&E pathology images, and that the segmentation quality is greatly improved by utilizing pathology foundation models. We discuss and propose methods for evaluating the performance of unsupervised segmentation in pathology.
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