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Computer-aided detection and classification of microcalcification clusters on full field digital mammograms using deep convolution neural network
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Paper Abstract

Breast cancer is presently one of the most common cancer among women and has high morbidity and mortality worldwide. The emergence of microcalcifications (MCs) is an important early sign of breast cancer. In this study, a computer-aided detection and diagnosis (CAD) system is developed to automatically detect MC clusters (MCCs) and further providing cancer likelihood prediction. Firstly, each individual MC is detected using our previously designed MC detection system, which includes preprocessing, MC enhancement, MC candidate detection, false positive (FP) reduction of MCs and regional clustering procedures. Secondly, a deep convolution neural network (DCNN) is trained on 394 clinical high-resolution full field digital mammograms (FFDMs) containing biopsy-proven MCCs to discriminate MCC lesions. For cluster-based detection evaluation, a 90% sensitivity is obtained with a FP rate of 0.2 FPs per image. The classification performance of the whole system is validated on 70 cases and tested on 71 cases, and for case-based diagnosis evaluation, the area under the receiver operating characteristic curve (AUC) on validation and testing sets are 0.945 and 0.932, respectively. Different from previous literatures committing to finding and selecting effective features, the proposed method replaces manual feature extraction step by using deep convolution neural network. The obtained results demonstrate that the proposed method is effective in the automatically detection and classification of MCCs.

Paper Details

Date Published: 13 March 2019
PDF: 6 pages
Proc. SPIE 10950, Medical Imaging 2019: Computer-Aided Diagnosis, 109502J (13 March 2019); doi: 10.1117/12.2512355
Show Author Affiliations
Guanxiong Cai, Sun Yat-Sen Univ. (China)
Yanhui Guo, Univ. of Illinois at Springfield (United States)
Weiguo Chen, Southern Medical Univ. (China)
Hui Zeng, Southern Medical Univ. (China)
Yuanpin Zhou, Sun Yat-Sen Univ. (China)
Yao Lu, Sun Yat-Sen Univ. (China)


Published in SPIE Proceedings Vol. 10950:
Medical Imaging 2019: Computer-Aided Diagnosis
Kensaku Mori; Horst K. Hahn, Editor(s)

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