基于分解的分割：通过分解基础模型特征实现病理学无监督语义分割
Segmentation by Factorization: Unsupervised Semantic Segmentation for Pathology by Factorizing Foundation Model Features
AI总结：
本文提出 F-SEG，无需训练或微调即可将预训练模型（尤其病理学基础模型）的空间特征分解为掩码与概念特征，实现稳健的 H&E 病理图像无监督语义分割。
摘要：
我们提出了 Segmentation by Factorization（F-SEG），这是一种面向病理学的无监督分割方法，可从预训练深度学习模型生成分割掩码。F-SEG 使预训练深度神经网络（包括近期开发的病理学基础模型）能够用于语义分割。该方法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关联的概念特征来实现这一目标。我们利用在 The Cancer Genome Atlas Program（TCGA）上从多个深度学习模型提取的特征，针对多种聚类数量训练聚类模型，从而为 H&E 图像创建通用组织表型；随后展示了如何使用现成的深度学习模型，将这些聚类用于分解相应的分割掩码。结果表明，F-SEG 为 H&E 病理图像提供了稳健的无监督分割能力，并且利用病理学基础模型可显著提升分割质量。我们还讨论并提出了评估病理学中无监督分割性能的方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
