Paper Abstract and Keywords |
Presentation |
2022-01-27 13:54
[Short Paper]
Case-based Similar Image Retrieval for Pathological Images of Malignant Lymphoma Using Deep Metric Learning Noriaki Hashimoto (RIKEN), Yusuke Takagi, Hiroki Masuda (NITech), Hiroaki Miyoshi, Kei Kohno, Miharu Nagaishi, Kensaku Sato, Koichi Ohshima (Kurume Univ.), Hidekata Hontani (NITech), Ichiro Takeuchi (NITech/RIKEN) MI2021-78 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
We propose a novel method of case-based similar image retrieval for histopathological images of malignant lymphoma. We employ contrastive distance metric learning to incorporate immunohistochemical staining patterns as useful supervised information for defining appropriate similarity between heterogeneous malignant lymphoma cases. Moreover, we realize a case-based image retrieval method that can automatically focus on tumor-specific regions in the entire tissue slide using attention-based multiple instance learning. In the experiment with 249 malignant lymphoma patients, we confirmed that the proposed method exhibited higher evaluation measures than the baseline case-based similar image retrieval methods. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
Pathological image / Case-based similar image retrieval / Malignant lymphoma / Distance metric learning / Immunohistochemical stain / / / |
Reference Info. |
IEICE Tech. Rep., vol. 121, no. 347, MI2021-78, pp. 144-145, Jan. 2022. |
Paper # |
MI2021-78 |
Date of Issue |
2022-01-18 (MI) |
ISSN |
Online edition: ISSN 2432-6380 |
Copyright and reproduction |
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MI2021-78 |
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