Abstract：
In the present study, we propose a novel case-based similar image retrieval (SIR) method for hematoxylin and eosin (H&E) stained histopathological images of malignant lymphoma. When a whole slide image (WSI) is used as an input query, it is desirable to be able to retrieve similar cases by focusing on image patches in pathologically important regions such as tumor cells. To address this problem, we employ attention-based multiple instance learning, which enables us to focus on tumor-specific regions when the similarity between cases is computed. Moreover, we employ contrastive distance metric learning to incorporate immunohistochemical (IHC) staining patterns as useful supervised information for defining appropriate similarity between heterogeneous malignant lymphoma cases. In the experiment with 249 malignant lymphoma patients, we confirmed that the proposed method exhibited higher evaluation measures than the baseline case-based SIR methods. Furthermore, the subjective evaluation by pathologists revealed that our similarity measure using IHC staining patterns is appropriate for representing the similarity of H&E stained tissue images for malignant lymphoma.
摘要：
在本研究中,我们提出了一种新的基于病例的相似图像检索（SIR）方法，用于恶性淋巴瘤的苏木精和伊红（H＆E）染色的组织病理学图像。当整个幻灯片图像(WSI)用作输入查询时，希望能够通过聚焦于诸如肿瘤细胞的病理重要区域中的图像块来检索类似的病例。为了解决这个问题,我们采用基于注意力的多实例学习，这使我们能够在计算病例之间的相似性时专注于肿瘤特异性区域。此外，我们采用对比距离度量学习,将免疫组织化学(IHC)染色模式作为有用的监督信息,用于确定异质恶性淋巴瘤病例之间的适当相似性.在249例恶性淋巴瘤患者的实验中，我们证实,与基于基线病例的SIR方法相比,提出的方法表现出更高的评价指标.此外，病理学家的主观评估显示,我们使用IHC染色模式进行的相似性测量适用于表示恶性淋巴瘤的H&E染色组织图像的相似性.
