@inproceedings{f1c8e716fac047c394a3f2139b606b54,
title = "Stain-Robust Mitotic Figure Detection for the Mitosis Domain Generalization Challenge",
abstract = "The detection of mitotic figures from different scanners/sites remains an important topic of research, owing to its potential in assisting clinicians with tumour grading. The MItosis DOmain Generalization (MIDOG) challenge aims to test the robustness of detection models on unseen data from multiple scanners for this task. We present a short summary of the approach employed by the TIA Centre team to address this challenge. Our approach is based on a hybrid detection model, where mitotic candidates are segmented on stain normalised images, before being refined by a deep learning classifier. Cross-validation on the training images achieved the F1-score of 0.786 and 0.765 on the preliminary test set, demonstrating the generalizability of our model to unseen data from new scanners.",
keywords = "Deep learning, Domain generalization, MIDOG, Mitosis detection",
author = "Mostafa Jahanifar and Adam Shepard and Neda Zamanitajeddin and Bashir, \{R. M.Saad\} and Mohsin Bilal and Khurram, \{Syed Ali\} and Fayyaz Minhas and Nasir Rajpoot",
note = "Publisher Copyright: {\textcopyright} 2022, Springer Nature Switzerland AG.; 24th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2021 ; Conference date: 27-09-2021 Through 01-10-2021",
year = "2022",
doi = "10.1007/978-3-030-97281-3\_6",
language = "English",
isbn = "9783030972806",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "48--52",
editor = "Marc Aubreville and David Zimmerer and Mattias Heinrich",
booktitle = "Biomedical Image Registration, Domain Generalisation and Out-of-Distribution Analysis - MICCAI 2021 Challenges",
address = "Germany",
}