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International Conference on Medical Image Computing and Computer-Assisted Intervention

MICCAI 2022: Medical Image Computing and Computer Assisted Intervention – MICCAI 2022 pp 253–262Cite as

Weakly Supervised Segmentation by Tensor Graph Learning for Whole Slide Images

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13432))

Abstract

Semantic segmentation of whole slide images (WSIs) helps pathologists identify lesions and cancerous nests. However, training fully supervised segmentation networks usually requires plenty of pixel-level annotations, which consume lots of time and human efforts. Coming from tissues of different patients with large amounts of pixels, WSIs exhibit various patterns, resulting in intra-class heterogeneity and inter-class homogeneity. Meanwhile, most existing methods for WSIs focus on extracting a certain type of features, neglecting the relations between different features and their joint effect on segmentation. Therefore, we propose a novel weakly supervised network based on tensor graphs (WSNTG) for WSI segmentation. Using only sparse point annotations, it efficiently segments WSIs by superpixel-wise classification and credible node reweighting. To deal with the variability of WSIs, the proposed network represents multiple hand-crafted features and hierarchical features yielded by a pretrained Convolutional Neural Network (CNN). Particularly, it learns over the semi-labeled tensor graphs constructed on the hierarchical features to exploit nonlinear data structures and associations. It gains robustness via the tensor-graph Laplacian of the hand-crafted features superimposed on the segmentation loss. We evaluated WSNTG on two WSI datasets, DigestPath2019 and SICAPV2. Results show that it outperforms many fully supervised and weakly supervised methods with minimal point annotations in WSI segmentation. The codes are published at https://github.com/zqh369/WSNTG.

This work was funded by the “Chenguang Program” supported by Shanghai Education Development Foundation and Shanghai Municipal Education Commission under Grant 18CG38.

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Notes

  1. 1.

    https://digestpath2019.grand-challenge.org/Dataset.

  2. 2.

    https://data.mendeley.com/datasets/9xxm58dvs3/2s.

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Authors and Affiliations

  1. School of Computer Science and Technology, Donghua University, Shanghai, 201620, China

    Qinghua Zhang & Zhao Chen

Authors
  1. Qinghua Zhang
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  2. Zhao Chen
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Corresponding author

Correspondence to Zhao Chen .

Editor information

Editors and Affiliations

  1. Rochester Institute of Technology, Rochester, NY, USA

    Linwei Wang

  2. Chinese University of Hong Kong, Hong Kong, Hong Kong

    Qi Dou

  3. University of Virginia, Charlottesville, VA, USA

    P. Thomas Fletcher

  4. National Center for Tumor Diseases (NCT/UCC), Dresden, Germany

    Stefanie Speidel

  5. Case Western Reserve University, Cleveland, OH, USA

    Shuo Li

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Cite this paper

Zhang, Q., Chen, Z. (2022). Weakly Supervised Segmentation by Tensor Graph Learning for Whole Slide Images. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2022. MICCAI 2022. Lecture Notes in Computer Science, vol 13432. Springer, Cham. https://doi.org/10.1007/978-3-031-16434-7_25

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  • DOI: https://doi.org/10.1007/978-3-031-16434-7_25

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  • Online ISBN: 978-3-031-16434-7

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