Journal
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
Volume 25, Issue 2, Pages 358-370Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JBHI.2020.3027566
Keywords
Mitotic detection; attention mechanism; deep learning
Categories
Funding
- National Natural Science Foundation of China [61871274, 61801305, 81571758]
- National Natural Science Foundation of Guangdong Province [2020A1515010649, 2019A1515111205]
- Guangdong Pre-national project [2014GKXM054]
- Guangdong Province Key Laboratory of Popular High Performance Computers [2017B030314073]
- Guangdong Laboratory of Artificial Intelligence and Cyber-Economics (SZ), Shenzhen Peacock Plan [KQTD2016053112051497, KQTD2015033016104926]
- Shenzhen Key Basic Research Project [JCYJ201-80507184647636, JCYJ20190808165209410, 20190808145011259, JCYJ20180507184647636, GJHZ20190822095414576, JCYJ20170302153337765, JCYJ20170302150411789, JCYJ20170302142515949, GCZX2017040715180580, GJHZ20180418190529516, JSGG20180507183215520]
- NTUTSZU Joint Research Program [2020003]
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The text discusses a method using deep convolutional neural networks to automatically detect mitosis, identifying mitotic candidates for screening and achieving the best detection results on the dataset of the International Pattern Recognition Conference (ICPR) 2012 Mitosis Detection Competition.
Mitotic count is an important indicator for assessing the invasiveness of breast cancers. Currently, the number of mitoses is manually counted by pathologists, which is both tedious and time-consuming. To address this situation, we propose a fast and accurate method to automatically detect mitosis from the histopathological images. The proposed method can automatically identify mitotic candidates from histological sections for mitosis screening. Specifically, our method exploits deep convolutional neural networks to extract high-level features of mitosis to detect mitotic candidates. Then, we use spatial attention modules to re-encode mitotic features, which allows the model to learn more efficient features. Finally, we use multi-branch classification subnets to screen the mitosis. Compared to existing related methods in literature, our method obtains the best detection results on the dataset of the International Pattern Recognition Conference (ICPR) 2012 Mitosis Detection Competition. Code has been made available at: https://github.com/liushaomin/MitosisDetection.
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