A Novel Deep Learning-Based Mitosis Recognition Approach and Dataset for Uterine Leiomyosarcoma Histopathology

Abstract

Uterine leiomyosarcoma (ULMS) is the most common sarcoma of the uterus, with both a high malignant potential and poor prognosis. Its diagnosis is sometimes challenging owing to its resemblance to leiomyosarcoma, often being accompanied by benign smooth muscle neoplasms of the uterus. Pathologists diagnose and grade leiomyosarcoma based on three biomarkers (i.e., mitosis count, necrosis, and nuclear atypia). Among these biomarkers, mitosis count is the most important and challenging biomarker. In general, pathologists use the traditional manual counting method for the detection and counting of mitosis. This procedure is very time-consuming, tedious, and subjective. To overcome these challenges, artificial intelligence (AI) based methods have been developed that automatically detect mitosis. In this paper, we propose a new ULMS dataset and an AI-based approach for mitosis detection. We collected our dataset from a local medical facility in collaboration with highly trained pathologists. Preprocessing and annotations are performed using standard procedures, and a deep learning-based method is applied to provide baseline accuracies. The experimental results showed 0.7462 precision, 0.8981 recall, and 0.8151 F1-score. For research and development, the code and dataset have been made publicly available.

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