Tackling Mitosis Domain Generalization in Histopathology Images with Color Normalization
Abstract
In this report, we propose a method for mitosis detection in histopathology images of MIDOG2022 challenge dataset. Our method is based on unsupervised domain adaptation at inputlevel, and a combination of color normalization and object detection. We evaluate our method by using the preliminary test set including 20 cases and obtain 0.716 F1 score.
02 /Specification & Classification
Click any tag to browse matching papers- Assay
- Research Focus
- Approach
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- Mitosis detection in the wild using detection transformers
Vidushi Walia, Dhandapani Nandagopal, … Bhagat Singh Lali·Lecture notes in computer sciencePeer-reviewed·Jan 1, 2026
- Subphase-Labeled Mitotic Dataset for AI-powered Cell Division Analysis
Zsanett Zsófia Iván, Dominik Hirling, … Péter Horváth·Scientific DataPeer-reviewed·Mar 13, 2026
- Class-imbalanced Unsupervised and Semi-Supervised Domain Adaptation for Histopathology Images
M. Hosseini, A. Shafique, … H. Tizhoosh·bioRxiv·Mar 8, 2023
- stainlib: a python library for augmentation and normalization of histopathology H&E images
Sebastian Otálora, Niccolò Marini, … Manfredo Atzori·bioRxiv·May 17, 2022
- StainX: GPU-accelerated batch stain normalization for computational pathology at scale
Samir Moustafa, Yimin Zheng, André F. Rendeiro·bioRxiv·Aug 7, 2026
- Transfer Learning From Nucleus Detection To Classification In Histopathology Images
Safoora Yousefi, Yao Nie·IEEE International Symposium on Biomedical Imaging (ISBI)Peer-reviewed·Apr 1, 2019