Deep-learning convolution neural network for computer-aided detection of microcalcifications in digital breast tomosynthesis

Ravi K. Samala; Heang-Ping Chan; Lubomir M. Hadjiiski; Kenny Cha; Mark A. Helvie

doi:10.1117/12.2217092

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24 March 2016 Deep-learning convolution neural network for computer-aided detection of microcalcifications in digital breast tomosynthesis

Ravi K. Samala, Heang-Ping Chan, Lubomir M. Hadjiiski, Kenny Cha, Mark A. Helvie

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Proceedings Volume 9785, Medical Imaging 2016: Computer-Aided Diagnosis; 97850Y (2016) https://doi.org/10.1117/12.2217092
Event: SPIE Medical Imaging, 2016, San Diego, California, United States

Abstract

A deep learning convolution neural network (DLCNN) was designed to differentiate microcalcification candidates detected during the prescreening stage as true calcifications or false positives in a computer-aided detection (CAD) system for clustered microcalcifications. The microcalcification candidates were extracted from the planar projection image generated from the digital breast tomosynthesis volume reconstructed by a multiscale bilateral filtering regularized simultaneous algebraic reconstruction technique. For training and testing of the DLCNN, true microcalcifications are manually labeled for the data sets and false positives were obtained from the candidate objects identified by the CAD system at prescreening after exclusion of the true microcalcifications. The DLCNN architecture was selected by varying the number of filters, filter kernel sizes and gradient computation parameter in the convolution layers, resulting in a parameter space of 216 combinations. The exhaustive grid search method was used to select an optimal architecture within the parameter space studied, guided by the area under the receiver operating characteristic curve (AUC) as a figure-of-merit. The effects of varying different categories of the parameter space were analyzed. The selected DLCNN was compared with our previously designed CNN architecture for the test set. The AUCs of the CNN and DLCNN was 0.89 and 0.93, respectively. The improvement was statistically significant (p < 0.05).

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Ravi K. Samala, Heang-Ping Chan, Lubomir M. Hadjiiski, Kenny Cha, and Mark A. Helvie "Deep-learning convolution neural network for computer-aided detection of microcalcifications in digital breast tomosynthesis", Proc. SPIE 9785, Medical Imaging 2016: Computer-Aided Diagnosis, 97850Y (24 March 2016); https://doi.org/10.1117/12.2217092

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