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Related Experiment Video

Updated: Jul 21, 2025

Live Imaging of Mitosis in the Developing Mouse Embryonic Cortex
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A comprehensive multi-domain dataset for mitotic figure detection.

Marc Aubreville1, Frauke Wilm2,3, Nikolas Stathonikos4

  • 1Technische Hochschule Ingolstadt, Ingolstadt, Germany. marc.aubreville@thi.de.

Scientific Data
|July 25, 2023
PubMed
Summary

Automating mitotic figure counting in tumor histology is crucial. The new MIDOG++ dataset, featuring diverse tumor types and scanning methods, improves deep learning model generalizability across domains.

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Area of Science:

  • Digital pathology
  • Computational biology
  • Medical image analysis

Background:

  • Mitotic figures in tumor tissue are key prognostic indicators.
  • Automating mitotic figure detection is a significant research goal.
  • Deep learning models struggle with domain shifts from varied data sources.

Purpose of the Study:

  • Introduce the MIDOG++ dataset for mitotic figure detection.
  • Address domain shift challenges in automated pathology.
  • Enhance generalizability of deep learning models for tumor analysis.

Main Methods:

  • Developed the MIDOG++ dataset with 11,937 mitotic figure labels across 503 histological specimens.
  • Included seven diverse tumor types (e.g., breast carcinoma, lung carcinoma).
  • Utilized specimens processed in multiple labs with various scanners to simulate domain shift.

Main Results:

  • Evaluated state-of-the-art methods, confirming significant performance drops due to domain shifts in single-domain training.
  • Demonstrated considerable improvement in model generalizability using a leave-one-domain-out approach.
  • The MIDOG++ dataset is the first to encompass domain shifts from tumor types, labs, scanners, and species.

Conclusions:

  • The MIDOG++ dataset provides a robust benchmark for evaluating domain generalization in mitotic figure detection.
  • Addressing domain shift is critical for reliable deployment of AI in digital pathology.
  • This dataset will facilitate the development of more robust and widely applicable automated pathology tools.