24 March 2016 Computer-aided classification of mammographic masses using the deep learning technology: a preliminary study
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Abstract
Although mammography is the only clinically acceptable imaging modality used in the population-based breast cancer screening, its efficacy is quite controversy. One of the major challenges is how to help radiologists more accurately classify between benign and malignant lesions. The purpose of this study is to investigate a new mammographic mass classification scheme based on a deep learning method. In this study, we used an image dataset involving 560 regions of interest (ROIs) extracted from digital mammograms, which includes 280 malignant and 280 benign mass ROIs, respectively. An eight layer deep learning network was applied, which employs three pairs of convolution-max-pooling layers for automatic feature extraction and a multiple layer perception (MLP) classifier for feature categorization. In order to improve robustness of selected features, each convolution layer is connected with a max-pooling layer. A number of 20, 10, and 5 feature maps were utilized for the 1st, 2nd and 3rd convolution layer, respectively. The convolution networks are followed by a MLP classifier, which generates a classification score to predict likelihood of a ROI depicting a malignant mass. Among 560 ROIs, 420 ROIs were used as a training dataset and the remaining 140 ROIs were used as a validation dataset. The result shows that the new deep learning based classifier yielded an area under the receiver operation characteristic curve (AUC) of 0.810±0.036. This study demonstrated the potential superiority of using a deep learning based classifier to distinguish malignant and benign breast masses without segmenting the lesions and extracting the pre-defined image features.
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Yuchen Qiu, Yuchen Qiu, Shiju Yan, Shiju Yan, Maxine Tan, Maxine Tan, Samuel Cheng, Samuel Cheng, Hong Liu, Hong Liu, Bin Zheng, Bin Zheng, "Computer-aided classification of mammographic masses using the deep learning technology: a preliminary study", Proc. SPIE 9785, Medical Imaging 2016: Computer-Aided Diagnosis, 978520 (24 March 2016); doi: 10.1117/12.2216336; https://doi.org/10.1117/12.2216336
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