Abstract
Deep learning based mitotic figure detection methods have been utilized to automatically locate the cell in mitosis using hematoxylin & eosin (H&E) stained images. However, the model performance deteriorates due to the large variation of color tone and intensity in H&E images. In this work, we proposed a two stage mitotic figure detection framework by fusing a detector and a deep ensemble classification model. To alleviate the impact of color variation in H&E images, we utilize both stain normalization and data augmentation, leading model to learn color irrelevant features. The proposed model obtains an F1 score of 0.7550 on the preliminary testing set and 0.7069 on the final testing set.
Supported by the Fundamental Research Funds for the Central Universities, Xidian University, under Grant No. XJS201213.
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Liang, J. et al. (2022). Detecting Mitosis Against Domain Shift Using a Fused Detector and Deep Ensemble Classification Model for MIDOG Challenge. In: Aubreville, M., Zimmerer, D., Heinrich, M. (eds) Biomedical Image Registration, Domain Generalisation and Out-of-Distribution Analysis. MICCAI 2021. Lecture Notes in Computer Science(), vol 13166. Springer, Cham. https://doi.org/10.1007/978-3-030-97281-3_10
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DOI: https://doi.org/10.1007/978-3-030-97281-3_10
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