24 March 2016 An initial investigation on developing a new method to predict short-term breast cancer risk based on deep learning technology
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Abstract
In order to establish a new personalized breast cancer screening paradigm, it is critically important to accurately predict the short-term risk of a woman having image-detectable cancer after a negative mammographic screening. In this study, we developed and tested a novel short-term risk assessment model based on deep learning method. During the experiment, a number of 270 “prior” negative screening cases was assembled. In the next sequential (“current”) screening mammography, 135 cases were positive and 135 cases remained negative. These cases were randomly divided into a training set with 200 cases and a testing set with 70 cases. A deep learning based computer-aided diagnosis (CAD) scheme was then developed for the risk assessment, which consists of two modules: adaptive feature identification module and risk prediction module. The adaptive feature identification module is composed of three pairs of convolution-max-pooling layers, which contains 20, 10, and 5 feature maps respectively. The risk prediction module is implemented by a multiple layer perception (MLP) classifier, which produces a risk score to predict the likelihood of the woman developing short-term mammography-detectable cancer. The result shows that the new CAD-based risk model yielded a positive predictive value of 69.2% and a negative predictive value of 74.2%, with a total prediction accuracy of 71.4%. This study demonstrated that applying a new deep learning technology may have significant potential to develop a new short-term risk predicting scheme with improved performance in detecting early abnormal symptom from the negative mammograms.
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Yuchen Qiu, Yunzhi Wang, Shiju Yan, Maxine Tan, Samuel Cheng, Hong Liu, Bin Zheng, "An initial investigation on developing a new method to predict short-term breast cancer risk based on deep learning technology", Proc. SPIE 9785, Medical Imaging 2016: Computer-Aided Diagnosis, 978521 (24 March 2016); doi: 10.1117/12.2216275; https://doi.org/10.1117/12.2216275
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