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Mitosis Detection, Fast and Slow: Robust and Efficient Detection of Mitotic Figures by Jahanifar, Mostafa; Shephard, Adam; Zamanitajeddin, Neda; Graham, Simon; Raza, Shan E Ahmed; Minhas, Fayyaz; Rajpoot, Nasir is a scholarly article available to read on EtoBox.

Counting of mitotic figures is a fundamental step in grading and prognostication of several cancers. However, manual mitosis counting is tedious and time-consuming. In addition, variation in the appearance of mitotic figures causes a high degree of discordance among pathologists. With advances in deep learning models, several automatic mitosis detection algorithms have been proposed but they are sensitive to {\em domain shift} often seen in histology images. We propose a robust and efficient two-stage mitosis detection framework, which comprises mitosis candidate segmentation ({\em Detecting Fast}) and candidate refinement ({\em Detecting Slow}) stages. The proposed candidate segmentation model, termed \textit{EUNet}, is fast and accurate due to its architectural design. EUNet can precisely segment candidates at a lower resolution to considerably speed up candidate detection. Candidates are then refined using a deeper classifier network, EfficientNet-B7, in the second stage. We make sure both stages are robust against domain shift by incorporating domain generalization methods. We demonstrate state-of-the-art performance and generalizability of the proposed model on the three large

Author

Jahanifar, Mostafa; Shephard, Adam; Zamanitajeddin, Neda; Graham, Simon; Raza, Shan E Ahmed; Minhas, Fayyaz; Rajpoot, Nasir

Published

2022

Language

EN