Recent advances in whole-slide image (WSI) scanners and computational capabilities have significantly propelled the application of artificial intelligence in histopathology slide analysis. While these strides are promising, current supervised learning approaches for WSI analysis come with the challenge of exhaustively labeling high-resolution slides - a process that is both labor-intensive and time-consuming. In contrast, self-supervised learning (SSL) pretraining strategies are emerging as a viable alternative, given that they don't rely on explicit data annotations. These SSL strategies are quickly bridging the performance disparity with their supervised counterparts. In this context, we introduce an SSL framework. This framework aims for transferable representation learning and semantically meaningful clustering by synergizing invariance loss and clustering loss in WSI analysis. Notably, our approach outperforms common SSL methods in downstream classification and clustering tasks, as evidenced by tests on the Camelyon16 and a pancreatic cancer dataset. The code and additional details are accessible at: https://github.com/wwyi1828/CluSiam.

翻译：全切片图像扫描仪和计算能力的最新进展显著推动了人工智能在组织病理学切片分析中的应用。尽管这些进展前景广阔，但当前用于全切片图像分析的监督学习方法仍面临着对高分辨率切片进行详尽标注的挑战——这一过程既耗费人力又耗时。相比之下，自监督学习预训练策略正成为一种可行的替代方案，因为它们不依赖显式数据标注。这些自监督学习策略正在迅速缩小与监督学习方法之间的性能差距。在此背景下，我们提出了一种自监督学习框架。通过在全切片图像分析中协同使用不变性损失和聚类损失，该框架旨在实现可迁移的表征学习和语义上有意义的聚类。值得注意的是，我们的方法在Camelyon16数据集和一个胰腺癌数据集上的下游分类和聚类任务测试中，优于常见的自监督学习方法。相关代码和更多详情可访问：https://github.com/wwyi1828/CluSiam。