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Abstract #1633

A 3D convolutional neural network for diagnosing prostate cancer using volumetric T2-weighted MRI.

Pritesh Mehta1, Michela Antonelli1, Shonit Punwani2, and Sebastien Ourselin3

1Biomedical Engineering and Medical Physics, University College London, London, United Kingdom, 2UCL Centre for Medical Imaging, University College London, London, United Kingdom, 3School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom

In this work, we designed and evaluated a convolutional neural network for prostate cancer diagnosis using volumetric T2-weighted MRI. Our key contribution is a 3D implementation of a residual network (ResNet), optimised to perform a classification between patients with prostate cancer and patients with benign conditions. On this task, cross-validation on a dataset consisting of 240 patients, produced a mean area under the receiver operating characteristic curve of 0.78, which was on par with an experienced radiologist.

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