Contrasting Low and High-Resolution Features for HER2 Scoring using Deep Learning

• URL: http://arxiv.org/abs/2503.22069v1
• Date: Fri, 28 Mar 2025 01:24:08 GMT
• Title: Contrasting Low and High-Resolution Features for HER2 Scoring using Deep Learning
• Authors: Ekansh Chauhan, Anila Sharma, Amit Sharma, Vikas Nishadham, Asha Ghughtyal, Ankur Kumar, Gurudutt Gupta, Anurag Mehta, C. V. Jawahar, P. K. Vinod,
• Abstract summary: This study introduces the India Pathology Breast Cancer dataset (IPD-Breast), comprising of 1,272 IHC slides (HER2, ER, and PR)<n>The primary focus is on developing predictive models for HER2 3-way classification (0, Low, High) to enhance prognosis.<n> Evaluation of multiple deep learning models revealed that an end-to-end ConvNeXt network utilizing low-resolution IHC images achieved an AUC, F1, and accuracy of 91.79%, 83.52%, and 83.56%, respectively, for 3-way classification.
• Score: 25.004143604870457
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Breast cancer, the most common malignancy among women, requires precise detection and classification for effective treatment. Immunohistochemistry (IHC) biomarkers like HER2, ER, and PR are critical for identifying breast cancer subtypes. However, traditional IHC classification relies on pathologists' expertise, making it labor-intensive and subject to significant inter-observer variability. To address these challenges, this study introduces the India Pathology Breast Cancer Dataset (IPD-Breast), comprising of 1,272 IHC slides (HER2, ER, and PR) aimed at automating receptor status classification. The primary focus is on developing predictive models for HER2 3-way classification (0, Low, High) to enhance prognosis. Evaluation of multiple deep learning models revealed that an end-to-end ConvNeXt network utilizing low-resolution IHC images achieved an AUC, F1, and accuracy of 91.79%, 83.52%, and 83.56%, respectively, for 3-way classification, outperforming patch-based methods by over 5.35% in F1 score. This study highlights the potential of simple yet effective deep learning techniques to significantly improve accuracy and reproducibility in breast cancer classification, supporting their integration into clinical workflows for better patient outcomes.

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