13 September 2017 Deep learning in breast cancer risk assessment: evaluation of convolutional neural networks on a clinical dataset of full-field digital mammograms
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Abstract
To evaluate deep learning in the assessment of breast cancer risk in which convolutional neural networks (CNNs) with transfer learning are used to extract parenchymal characteristics directly from full-field digital mammographic (FFDM) images instead of using computerized radiographic texture analysis (RTA), 456 clinical FFDM cases were included: a “high-risk” BRCA1/2 gene-mutation carriers dataset (53 cases), a “high-risk” unilateral cancer patients dataset (75 cases), and a “low-risk dataset” (328 cases). Deep learning was compared to the use of features from RTA, as well as to a combination of both in the task of distinguishing between high- and low-risk subjects. Similar classification performances were obtained using CNN [area under the curve (AUC)=0.83; standard error (SE)=0.03] and RTA (AUC=0.82; SE=0.03) in distinguishing BRCA1/2 carriers and low-risk women. However, in distinguishing unilateral cancer patients and low-risk women, performance was significantly greater with CNN (AUC=0.82; SE=0.03) compared to RTA (AUC=0.73; SE=0.03). Fusion classifiers performed significantly better than the RTA-alone classifiers with AUC values of 0.86 and 0.84 in differentiating BRCA1/2 carriers from low-risk women and unilateral cancer patients from low-risk women, respectively. In conclusion, deep learning extracted parenchymal characteristics from FFDMs performed as well as, or better than, conventional texture analysis in the task of distinguishing between cancer risk populations.
© 2017 Society of Photo-Optical Instrumentation Engineers (SPIE)
Hui Li, Hui Li, Maryellen L. Giger, Maryellen L. Giger, Benjamin Q. Huynh, Benjamin Q. Huynh, Natasha O. Antropova, Natasha O. Antropova, } "Deep learning in breast cancer risk assessment: evaluation of convolutional neural networks on a clinical dataset of full-field digital mammograms," Journal of Medical Imaging 4(4), 041304 (13 September 2017). https://doi.org/10.1117/1.JMI.4.4.041304 . Submission: Received: 7 February 2017; Accepted: 18 August 2017
Received: 7 February 2017; Accepted: 18 August 2017; Published: 13 September 2017
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