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In the present study, we propose a novel case-based similar image retrieval(SIR) method for hematoxylin and eosin (H&E)-stained histopathological imagesof malignant lymphoma. When a whole slide image (WSI) is used as an inputquery, it is desirable to be able to retrieve similar cases by focusing onimage patches in pathologically important regions such as tumor cells. Toaddress this problem, we employ attention-based multiple instance learning,which enables us to focus on tumor-specific regions when the similarity betweencases is computed. Moreover, we employ contrastive distance metric learning toincorporate immunohistochemical (IHC) staining patterns as useful supervisedinformation for defining appropriate similarity between heterogeneous malignantlymphoma cases. In the experiment with 249 malignant lymphoma patients, weconfirmed that the proposed method exhibited higher evaluation measures thanthe baseline case-based SIR methods. Furthermore, the subjective evaluation bypathologists revealed that our similarity measure using IHC staining patternsis appropriate for representing the similarity of H&E-stained tissue images formalignant lymphoma.

Case-based Similar Image Retrieval for Weakly Annotated Large Histopathological Images of Malignant Lymphoma Using Deep Metric Learning