GrapHist: 基于图的病理学自监督学习
GrapHist: Graph Self-Supervised Learning for Histopathology
AI总结：
GrapHist通过基于图的自监督学习方法，在病理学中实现高效的表示学习，适用于多种下游任务，且参数更少，性能更优。
摘要：
自监督视觉模型在数字病理学中取得了显著成功。然而，其领域无关的Transformer架构并非最初设计用于考虑组织病理学图像的基本生物元素，即细胞及其复杂的相互作用。在本工作中，我们假设将组织建模为细胞图，以更高效的方式进行表示学习。因此，我们引入了GrapHist，一种新的基于图的自监督学习框架，用于病理学，它学习可推广且结构信息丰富的嵌入，以支持多种下游任务。GrapHist集成了屏蔽自动编码器和异质图神经网络，这些网络专门设计用于捕捉肿瘤微环境的异质性。我们预先在由乳腺组织衍生出的1100万个细胞图的大型集合上训练GrapHist，并在输入和输出领域基准上评估其可转移性。我们的结果表明，GrapHist在切片、区域和细胞层面的任务中与基于视觉的方法相比具有竞争力，同时参数数量少四倍。它还在癌症亚型分类任务中明显优于全监督图模型。最后，我们还发布了五个用于本研究的基于图的数字病理学数据集，在https://huggingface.co/ogutsevda/datasets上发布，建立了该领域的首个大规模图基准。我们的代码可在https://github.com/ogutsevda/graphist上获得。
英文摘要：
Self-supervised vision models have achieved notable success in digital pathology. However, their domain-agnostic transformer architectures are not originally designed to account for fundamental biological elements of histopathology images, namely cells and their complex interactions. In this work, we hypothesize that a biologically-informed modeling of tissues as cell graphs offers a more efficient representation learning. Thus, we introduce GrapHist, a novel graph-based self-supervised learning framework for histopathology, which learns generalizable and structurally-informed embeddings that enable diverse downstream tasks. GrapHist integrates masked autoencoders and heterophilic graph neural networks that are explicitly designed to capture the heterogeneity of tumor microenvironments. We pre-train GrapHist on a large collection of 11 million cell graphs derived from breast tissues and evaluate its transferability across in- and out-of-domain benchmarks. Our results show that GrapHist achieves competitive performance compared to its vision-based counterparts in slide-, region-, and cell-level tasks, while requiring four times fewer parameters. It also drastically outperforms fully-supervised graph models on cancer subtyping tasks. Finally, we also release five graph-based digital pathology datasets used in our study at https://huggingface.co/ogutsevda/datasets , establishing the first large-scale graph benchmark in this field. Our code is available at https://github.com/ogutsevda/graphist .
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。
