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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