Hashimoto Noriaki, Takagi Yusuke, Masuda Hiroki, Miyoshi Hiroaki, Kohno Kei, Nagaishi Miharu, Sato Kensaku, Takeuchi Mai, Furuta Takuya, Kawamoto Keisuke, Yamada Kyohei, Moritsubo Mayuko, Inoue Kanako, Shimasaki Yasumasa, Ogura Yusuke, Imamoto Teppei, Mishina Tatsuzo, Tanaka Ken, Kawaguchi Yoshino, Nakamura Shigeo, Ohshima Koichi, Hontani Hidekata, Takeuchi Ichiro
RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan.
Department of Computer Science, Nagoya Institute of Technology, Gokiso-cho, Showa-ku, Nagoya 466-8555, Japan.
Med Image Anal. 2023 Apr;85:102752. doi: 10.1016/j.media.2023.102752. Epub 2023 Jan 25.
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.
在本研究中，我们提出了一种新颖的基于案例的相似图像检索（SIR）方法，用于恶性淋巴瘤苏木精和伊红（H&E）染色的组织病理学图像。当将全切片图像（WSI）用作输入查询时，期望能够通过关注病理重要区域（如肿瘤细胞）中的图像块来检索相似病例。为了解决这个问题，我们采用基于注意力的多实例学习，这使我们在计算病例之间的相似性时能够关注肿瘤特异性区域。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，用于定义异质性恶性淋巴瘤病例之间的适当相似性。在对249例恶性淋巴瘤患者的实验中，我们证实所提出的方法比基于案例的基线SIR方法具有更高的评估指标。此外，病理学家的主观评估表明，我们使用IHC染色模式的相似性度量适用于表示恶性淋巴瘤H&E染色组织图像的相似性。
