Presentation + Paper
6 April 2023 Multi-scale contrastive learning with attention for histopathology image classification
Jing Wei Tan, Khoa Tuan Nguyen, Kyoungbun Lee, Won-Ki Jeong
Author Affiliations +
Abstract
Whole slide images (WSIs) in histopathology naturally provide multi-scale information. Several previous studies have shown that leveraging such multi-scale information in histopathology image analysis is effective to improve performance. Here, we propose making use of recent advances in contrastive learning and self-attention techniques in multi-scale WSIs for cancer subtype classification using weak labels. The proposed method is based on a Siamese architecture to share a common encoder network for images on different scales to reduce the model size and training cost. In addition, we propose a variant of the self-attention module specifically designed for multi-scale WSIs so that the network can focus on important textural features across different image scales. We assess the efficacy of the proposed method via an ablation study on a real intrahepatic cholangiocarcinoma dataset. The result confirms that our method outperforms conventional multi-scale models with fewer model parameters.
Conference Presentation
© (2023) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Jing Wei Tan, Khoa Tuan Nguyen, Kyoungbun Lee, and Won-Ki Jeong "Multi-scale contrastive learning with attention for histopathology image classification", Proc. SPIE 12471, Medical Imaging 2023: Digital and Computational Pathology, 1247115 (6 April 2023); https://doi.org/10.1117/12.2653423
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KEYWORDS
Feature extraction

Tumors

Histopathology

Image classification

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