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Weakly supervised segmentation on neural compressed histopathology with self-equivariant regularization

Weakly supervised segmentation on neural compressed histopathology with self-equivariant regularization
Chikontwe, PhilipSung, Hyun JungJeong, JaehoonKim, MeejeongGo, HeounjeongNam, Soo JeongPark, Sang Hyun
Issued Date
Medical Image Analysis, v.80
Author Keywords
Class activation mapCompressed histopathologyDeep learningImage segmentationWeakly supervised learning
In digital pathology, segmentation is a fundamental task for the diagnosis and treatment of diseases. Existing fully supervised methods often require accurate pixel-level annotations that are both time-consuming and laborious to generate. Typical approaches first pre-process histology images into patches to meet memory constraints and later perform stitching for segmentation; at times leading to lower performance given the lack of global context. Since image level labels are cheaper to acquire, weakly supervised learning is a more practical alternative for training segmentation algorithms. In this work, we present a weakly supervised framework for histopathology segmentation using only image-level labels by refining class activation maps (CAM) with self-supervision. First, we compress gigapixel histology images with an unsupervised contrastive learning technique to retain high-level spatial context. Second, a network is trained on the compressed images to jointly predict image-labels and refine the initial CAMs via self-supervised losses. In particular, we achieve refinement via a pixel correlation module (PCM) that leverages self-attention between the initial CAM and the input to encourage fine-grained activations. Also, we introduce a feature masking technique that performs spatial dropout on the compressed input to suppress low confidence predictions. To effectively train our model, we propose a loss function that includes a classification objective with image-labels, self-supervised regularization and entropy minimization between the CAM predictions. Experimental results on two curated datasets show that our approach is comparable to fully-supervised methods and can outperform existing state-of-the-art patch-based methods. © 2022 Elsevier B.V.
Elsevier BV
Related Researcher
  • 박상현 Park, Sang Hyun 로봇및기계전자공학과
  • Research Interests 컴퓨터비전; 인공지능; 의료영상처리
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Department of Robotics and Mechatronics Engineering Medical Image & Signal Processing Lab 1. Journal Articles


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