Breast Histopathology Image Retrieval by Attention-based Adversarially Regularized Variational Graph Autoencoder with Contrastive Learning-Based Feature Extraction

• URL: http://arxiv.org/abs/2405.04211v2
• Date: Mon, 10 Jun 2024 08:52:58 GMT
• Title: Breast Histopathology Image Retrieval by Attention-based Adversarially Regularized Variational Graph Autoencoder with Contrastive Learning-Based Feature Extraction
• Authors: Nematollah Saeidi, Hossein Karshenas, Bijan Shoushtarian, Sepideh Hatamikia, Ramona Woitek, Amirreza Mahbod,
• Abstract summary: This work introduces a novel attention-based adversarially regularized variational graph autoencoder model for breast histological image retrieval. We evaluated the performance of the proposed model on two publicly available datasets of breast cancer histological images.
• Score: 1.48419209885019
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Breast cancer is the most common cancer type in women worldwide. Early detection and appropriate treatment can significantly reduce its impact. While histopathology examinations play a vital role in rapid and accurate diagnosis, they often require a substantial workforce and experienced medical experts for proper recognition and cancer grading. Automated image retrieval systems have the potential to assist pathologists in identifying cancerous tissues, thereby accelerating the diagnostic process. Nevertheless, due to considerable variability among the tissue and cell patterns in histological images, proposing an accurate image retrieval model is very challenging. This work introduces a novel attention-based adversarially regularized variational graph autoencoder model for breast histological image retrieval. Additionally, we incorporated cluster-guided contrastive learning as the graph feature extractor to boost the retrieval performance. We evaluated the performance of the proposed model on two publicly available datasets of breast cancer histological images and achieved superior or very competitive retrieval performance, with average mAP scores of 96.5% for the BreakHis dataset and 94.7% for the BACH dataset, and mVP scores of 91.9% and 91.3%, respectively. Our proposed retrieval model has the potential to be used in clinical settings to enhance diagnostic performance and ultimately benefit patients.

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