@article{1290,
  author = {Zsanett Ivan and Dominik Hirling and Istvan Grexa and Jonas Ammeling and Csaba Molnar and Tamas Micsik and Katalin Dobra and Levente Kuthi and Farkas Sukosd and Janos Fillinger and Judit Moldvay and Erika Toth and Marc Aubreville and Vivien Miczan and Peter Horvath},
  title = {A Subphase-Labeled Mitotic Dataset for AI-powered Cell Division Analysis},
  abstract = {Abstract Mitosis detection represents a critical task in digital pathology, as it plays an important role in the tumor grading and prognosis of patients. Manual determination is a labor-intensive task for practitioners with high interobserver variability, thus, automation is a priority. There has been substantial progress towards creating robust mitosis detection algorithms, primarily driven by the Mitosis Domain Generalization (MIDOG) challenges. Also, there has been growing interest in the molecular characterization of mitosis to achieve a more comprehensive understanding of its underlying mechanisms in a subphase-specific manner. We introduce a new mitotic figure dataset annotated with subphase information based on the MIDOG++ dataset as well as a previously unrepresented tumor domain to enhance the diversity and applicability. We envision a new perspective for domain generalization by improving model performance with subtyping mitosis, complemented with an atypical mitotic class. Our work has implications in two main areas: subtyping information can provide helpful information in mitosis detection, while also providing promising new directions in answering biological questions, such as molecular analysis of subphases.},
  year = {2026},
  booktitle = {Scientific Data},
  journal = {Scientific Data},
  issn = {2052-4463},
  url = {https://www.nature.com/articles/s41597-026-07007-7},
  doi = {10.1038/s41597-026-07007-7},
}