ArticleCancers2022
A Novel Deep Learning-Based Mitosis Recognition Approach and Dataset for Uterine Leiomyosarcoma Histopathology.
Article in Cancers, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Liquid Biopsy in Uterine Leiomyosarcoma: Current Biomarkers, Emerging Technologies, and Future Perspectives.Current oncology reports · 2026Review
- AI in Histopathology Explorer for comprehensive analysis of the evolving AI landscape in histopathology.NPJ digital medicine · 2025Article
- The Impact of Artificial Intelligence on Cancer Diagnosis and Treatment: A Review.Cancer informatics · 2025Review
- Model for detecting metastatic deposits in lymph nodes of colorectal carcinoma on digital/ non-WSI images.Diagnostic pathology · 2024Article
- Artificial intelligence-based automated determination in breast and colon cancer and distinction between atypical and typical mitosis using a cloud-based platform.Pathology oncology research : POR · 2024Article
- Artificial Intelligence in the Pathology of Gastric Cancer.Journal of gastric cancer · 2023Review
- A suggested way forward for adoption of AI-Enabled digital pathology in low resource organizations in the developing world.Diagnostic pathology · 2023Review
- Use of Novel Open-Source Deep Learning Platform for Quantification of Ki-67 in Neuroendocrine Tumors - Analytical Validation.International journal of general medicine · 2023Article
- Machine Learning Methods in Predicting Patients with Suspected Myocardial Infarction Based on Short-Time HRV Data.Sensors (Basel, Switzerland) · 2022Article
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Authors and funding
8 authors.
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Abstract
Uterine leiomyosarcoma (ULMS) is the most common sarcoma of the uterus, It is aggressive and has poor prognosis. Its diagnosis is sometimes challenging owing to its resemblance by benign smooth muscle neoplasms of the uterus. Pathologists diagnose and grade leiomyosarcoma based on three standard criteria (i.e., mitosis count, necrosis, and nuclear atypia). Among these, 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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.