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	Mitosis Detection in Breast Cancer Histology Images via Deep Cascaded Networks
	Hao Chen; Qi Dou; Xi Wang; Jing Qin; Pheng-Ann Heng
	2016
会议名称	In Thirtieth AAAI Conference on Artificial Intelligence.
会议地点	Phoenix, Arizona USA
英文摘要	The number of mitoses per tissue area gives an important aggressiveness indication of the invasive breast carcinoma. However, automatic mitosis detection in histology images remains a challenging problem. Traditional methods either employ hand-crafted features to discriminate mitoses from other cells or construct a pixelwise classifier to label every pixel in a sliding window way. While the former suffers from the large shape variation of mitoses and the existence of many mimics with similar appearance, the slow speed of the later prohibits its use in clinical practice. In order to overcome these shortcomings, we propose a fast and accurate method to detect mitosis by designing a novel deep cascaded convolutional neural network, which is composed of two components. First, by leveraging the fully convolutional neural network, we propose a coarse retrieval model to identify and locate the candidates of mitosis while preserving a high sensitivity. Based on these candidates, a fine discrimination model utilizing knowledge transferred from cross-domain is developed to further single out mitoses from hard mimics. Our approach outperformed other methods by a large margin in 2014 ICPR MITOS-ATYPIA challenge in terms of detection accuracy. When compared with the state-of-the-art methods on the 2012 ICPR MITOSIS data (a smaller and less challenging dataset), our method achieved comparable or better results with a roughly 60 times faster speed.
收录类别	EI
语种	英语
内容类型	会议论文
源URL	[http://ir.siat.ac.cn:8080/handle/172644/10045]
专题	深圳先进技术研究院_集成所
作者单位	2016
推荐引用方式 GB/T 7714	Hao Chen,Qi Dou,Xi Wang,et al. Mitosis Detection in Breast Cancer Histology Images via Deep Cascaded Networks[C]. 见:In Thirtieth AAAI Conference on Artificial Intelligence.. Phoenix, Arizona USA.

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