mlsanityData to 14 Oct 2026

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

arXiv 2107.03602 ↗First posted 8 Jul 2021cs.CVcited by 3

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Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 16 of its 58 references that are in the map. First posted on arXiv 8 Jul 2021.

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16 of 58 references.

References · 16

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4 more
  1. 2019Computing Valid p-values for Image Segmentation by Selective Inferencecited by 24
  2. 2020Structure Preserving Stain Normalization of Histopathology Images Using Self-Supervised Semantic Guidancecited by 22
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Citing papers per year: 2022: 1; 2023: 1; 2024: 1.

3 papers.

  1. 2022Transformer-based Personalized Attention Mechanism for Medical Images with Clinical Recordscited by 2
  2. 2023A vision transformer-based framework for knowledge transfer from multi-modal to mono-modal lymphoma subtyping modelscited by 1
  3. 2024CBIDR: A novel method for information retrieval combining image and data by means of TOPSIS applied to medical diagnosiscited by 0