IEEE Trans Pattern Anal Mach Intell. 2023 Sep;45(9):11008-11023. doi: 10.1109/TPAMI.2023.3269810. Epub 2023 Aug 7.
Histopathological Whole Slide Images (WSIs) play a crucial role in cancer diagnosis. It is of significant importance for pathologists to search for images sharing similar content with the query WSI, especially in the case-based diagnosis. While slide-level retrieval could be more intuitive and practical in clinical applications, most methods are designed for patch-level retrieval. A few recently unsupervised slide-level methods only focus on integrating patch features directly, without perceiving slide-level information, and thus severely limits the performance of WSI retrieval. To tackle the issue, we propose a High-Order Correlation-Guided Self-Supervised Hashing-Encoding Retrieval (HSHR) method. Specifically, we train an attention-based hash encoder with slide-level representation in a self-supervised manner, enabling it to generate more representative slide-level hash codes of cluster centers and assign weights for each. These optimized and weighted codes are leveraged to establish a similarity-based hypergraph, in which a hypergraph-guided retrieval module is adopted to explore high-order correlations in the multi-pairwise manifold to conduct WSI retrieval. Extensive experiments on multiple TCGA datasets with over 24,000 WSIs spanning 30 cancer subtypes demonstrate that HSHR achieves state-of-the-art performance compared with other unsupervised histology WSI retrieval methods.
组织病理学全切片图像（WSI）在癌症诊断中起着至关重要的作用。对于病理学家来说，搜索与查询 WSI 具有相似内容的图像非常重要，特别是在基于案例的诊断中。虽然在临床应用中，幻灯片级别的检索可能更直观和实用，但大多数方法都是为补丁级别的检索而设计的。最近有几种无监督的幻灯片级别的方法仅关注直接整合补丁特征，而没有感知幻灯片级别的信息，因此严重限制了 WSI 检索的性能。为了解决这个问题，我们提出了一种基于高阶相关的自监督哈希编码检索（HSHR）方法。具体来说，我们以自监督的方式训练一个具有幻灯片级表示的基于注意力的哈希编码器，使其能够生成更具代表性的聚类中心的幻灯片级哈希码，并为每个哈希码分配权重。这些优化后的加权代码被用于建立基于相似性的超图，其中采用超图引导的检索模块来探索多对多流形中的高阶相关性，以进行 WSI 检索。在多个包含超过 30 种癌症亚型的 24000 多个 WSI 的 TCGA 数据集上进行的广泛实验表明，与其他无监督组织学 WSI 检索方法相比，HSHR 具有最先进的性能。
