ArticleScientific data2023
A comprehensive multi-domain dataset for mitotic figure detection.
Article in Scientific data, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- A human epithelial cell cycle dataset.Scientific data · 2026Article
- Metric discordance under MIDOG++ domain shift: a leave-one-domain-out audit of F1, emitted-detection confidence calibration, and ROI count error.Journal of pathology informatics · 2026Article
- Applicability of mitotic figure counting by deep learning: a development and pan-cancer validation study.FEBS open bio · 2026Article
- Recent Advances in Automated Mitosis Detection in Digital Pathology: A PRISMA-Guided Systematic Review with Evaluation-Regime Stratification (2018-2025).Biomedicines · 2026Review
- Scalable and Efficient Deep Learning-Based Pipeline for Mitotic Detection and Analysis in Pathology Images.Cancers · 2026Article
- A Subphase-Labeled Mitotic Dataset for AI-powered Cell Division Analysis.Scientific data · 2026Article
- Adaptive example selection for prototype based explainable mitosis detection in digital pathology.Scientific reports · 2026Article
- Computer Vision-Assisted Spatial Analysis of Mitoses and Vasculature in Lung Cancer.Journal of clinical medicine · 2025Article
- Role of artificial intelligence in screening and medical imaging of precancerous gastric diseases.World journal of clinical oncology · 2025Review
- Automated mitosis detection in stained histopathological images using Faster R-CNN and stain techniques.Journal of integrative bioinformatics · 2025Article
- Nuclear pleomorphism in canine cutaneous mast cell tumors: Comparison of reproducibility and prognostic relevance between estimates, manual morphometry, and algorithmic morphometry.Veterinary pathology · 2025Article
- Machine learning methods for histopathological image analysis: Updates in 2024.Computational and structural biotechnology journal · 2025Review
- A deep learning framework deploying segment anything to detect pan-cancer mitotic figures from haematoxylin and eosin-stained slides.Communications biology · 2024Article
- The 1000 Mitoses Project: A Consensus-Based International Collaborative Study on Mitotic Figures Classification.International journal of surgical pathology · 2024Article
- Breast cancer survival prediction using an automated mitosis detection pipeline.The journal of pathology. Clinical research · 2024Article
- A foundation model for clinical-grade computational pathology and rare cancers detection.Nature medicine · 2024Article
- Registered multi-device/staining histology image dataset for domain-agnostic machine learning models.Scientific data · 2024Article
- Article
- Enhancing mitosis quantification and detection in meningiomas with computational digital pathology.Acta neuropathologica communications · 2024Article
- Atypical Mitotic Figures Are Prognostically Meaningful for Canine Cutaneous Mast Cell Tumors.Veterinary sciences · 2023Article
Corrections and comments
- Erratum issued
Authors and funding
12 authors.
Funding
Abstract
The prognostic value of mitotic figures in tumor tissue is well-established for many tumor types and automating this task is of high research interest. However, especially deep learning-based methods face performance deterioration in the presence of domain shifts, which may arise from different tumor types, slide preparation and digitization devices. We introduce the MIDOG++ dataset, an extension of the MIDOG 2021 and 2022 challenge datasets. We provide region of interest images from 503 histological specimens of seven different tumor types with variable morphology with in total labels for 11,937 mitotic figures: breast carcinoma, lung carcinoma, lymphosarcoma, neuroendocrine tumor, cutaneous mast cell tumor, cutaneous melanoma, and (sub)cutaneous soft tissue sarcoma. The specimens were processed in several laboratories utilizing diverse scanners. We evaluated the extent of the domain shift by using state-of-the-art approaches, observing notable differences in single-domain training. In a leave-one-domain-out setting, generalizability improved considerably. This mitotic figure dataset is the first that incorporates a wide domain shift based on different tumor types, laboratories, whole slide image scanners, and species.
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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.