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Abstract #4815

Automatic classification of benign and malignant prostate lesions: A comparison using VERDICT DW-MRI and ADC maps

Eleni Chiou1,2, Edward Johnston3, Francesco Giganti4,5, Elisenda Bonet-Carne1, Shonit Punwani3, Iasonas Kokkinos2, and Eleftheria Panagiotaki1,2

1UCL Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom, 2Department of Computer Science, University College London, London, United Kingdom, 3UCL Centre for Medical Imaging, Division of Medicine, University College London, London, United Kingdom, 4Department of Radiology, UCLH NHS Foundation Trust, University College London, London, United Kingdom, 5Division of Surgery & Interventional Science, University College London, London, United Kingdom

Currently, many studies exploit deep learning and mp-MRI data to enhance the diagnostic accuracy of prostate cancer characterisation. In this study, we focus on VERDICT DW-MRI data and compare its diagnostic performance to those of the ADC map and the raw DW-MRI from the mp-MRI. Specifically, we compare the performance obtained by a fully convolutional neural network (CNN) when training and test is performed on the raw VERDICT DW-MRI, the ADC maps and the DW-MRI data from the mp-MRI acquisition. The results indicate that the CNN performs better when it is trained and tested on VERDICT DW-MRI.

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