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A computer-aided diagnosis system for breast pathology: a deep learning approach with model interpretability from pathological perspective

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Abstract

In this study, a computer-aided diagnosis (CAD) system for whole-slide images (WSIs) with breast cancer was proposed to achieve lesion detection and classification. Different from the previously proposed works that performed segmentation directly and applied the segmentation metrics for performance evaluation, here we computed the accuracy in patch-level, region-level, and slide-level to access our proposed framework to meet the clinical requirements, instead of simply regarding it as an AI segmentation task. To achieve that for WSIs, lesion detection was conducted at high magnification (× 40) first for region proposal by patching using the modified AlexNet model; and then, several patches were sampled from the detected regions at lower magnification (× 10) to observe cells’ patterns for lesion classification by the model of ResNet50. With such fashion, the deep features being distinguishing in lesion classification from the convolutional neural networks (CNN) were further analyzed in this work to provide comprehensive interpretability for the proposed CAD system based on pathological knowledge. For our experiments, a total of 186 slides of WSIs were collected and classified into three categories: Non-Carcinoma, Ductal Carcinoma in Situ (DCIS), and Invasive Ductal Carcinoma (IDC). The slide-level accuracy rate for the three-category classification reached 90.8% (99/109) through a fivefold cross-validation and achieved 94.8% (73/77) on the testing set. In addition, the learned features reflect the clinical insights in lesion classification for explainable AI, which enhanced the reliability and validity of our proposed system.

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Funding

This research was funded by the National Natural Science Foundation of China (Grant No. 81972485).

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Author notes

  1. Wei-Wen Hsu and Yongfang Wu have contributed equally.

Authors and Affiliations

  1. Department of Computer Science and Information Technology, National Taitung University, Taitung, Taiwan

    Wei-Wen Hsu

  2. Department of Pathology, The Fifth Medical Center of Chinese, PLA General Hospital, Beijing, China

    Yongfang Wu, Yun Shao, Xueli Zhang & Yanhong Tai

  3. Department of Electrical and Computer Engineering, Old Dominion University, Norfolk, VA, USA

    Chung-Hao Chen

  4. Institute of Big Data Technology, Bright Oceans Corporation, Beijing, China

    Chang Hao, Yu-Ling Hou, Xiang Gao & Tao He

Authors
  1. Wei-Wen Hsu
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  4. Chang Hao
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Contributions

Conceptualization contributed by W-WH, YW, TH and YT; methodology contributed by W-WH, C-HC and Y-LH; software contributed by W-WH and CH; formal analysis contributed by YW, XG and YT; labeling contributed by YW, YS and XZ; investigation contributed by W-WH and CH; writing—original draft preparation contributed by W-WH and YW; writing—review and editing contributed by W-WH and YT; supervision contributed by YT; project administration contributed by YT; funding acquisition contributed by YT. All authors have read and agreed to the published version of the manuscript.

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Correspondence to Yanhong Tai.

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Hsu, WW., Wu, Y., Chen, CH. et al. A computer-aided diagnosis system for breast pathology: a deep learning approach with model interpretability from pathological perspective. SOCA 18, 183–193 (2024). https://doi.org/10.1007/s11761-023-00378-4

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