Title

Deep recurrent neural networks for prostate cancer detection: Analysis of temporal enhanced ultrasound

Document Type

Journal Article

Publication Date

12-1-2018

Journal

IEEE Transactions on Medical Imaging

Volume

37

Issue

12

DOI

10.1109/TMI.2018.2849959

Keywords

cancer detection; deep learning; long short-term memory; prostate cancer; recurrent neural network; Temporal enhanced ultrasound

Abstract

© 2018 IEEE. Temporal enhanced ultrasound (TeUS), comprising the analysis of variations in backscattered signals from a tissue over a sequence of ultrasound frames, has been previously proposed as a new paradigm for tissue characterization. In this paper, we propose to use deep recurrent neural networks (RNN) to explicitly model the temporal information in TeUS. By investigating several RNN models, we demonstrate that long short-term memory (LSTM) networks achieve the highest accuracy in separating cancer from benign tissue in the prostate. We also present algorithms for in-depth analysis of LSTM networks. Our in vivo study includes data from 255 prostate biopsy cores of 157 patients. We achieve area under the curve, sensitivity, specificity, and accuracy of 0.96, 0.76, 0.98, and 0.93, respectively. Our result suggests that temporal modeling of TeUS using RNN can significantly improve cancer detection accuracy over previously presented works.

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