Stain-Robust Mitotic Figure Detection for the Mitosis Domain Generalization Challenge

Mostafa Jahanifar; Adam Shephard; Neda Zamani Tajeddin; R. M. Saad Bashir; Mohsin Bilal; Syed Ali Khurram; Fayyaz Minhas; Nasir Rajpoot

arXiv · 2109.00853

Stain-Robust Mitotic Figure Detection for the Mitosis Domain Generalization Challenge

Mostafa Jahanifar·Adam Shephard·Neda Zamani Tajeddin·R. M. Saad Bashir·Mohsin Bilal·Syed Ali Khurram·Fayyaz Minhas·Nasir Rajpoot

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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.

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Mostafa Jahanifar, Adam Shephard, Neda Zamani Tajeddin, R. M. Saad Bashir, Mohsin Bilal, Syed Ali Khurram, Fayyaz Minhas, Nasir Rajpoot. 2021-09-02. Stain-Robust Mitotic Figure Detection for the Mitosis Domain Generalization Challenge. https://arxiv.org/abs/2109.00853

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