UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

Prostate Cancer Classification on VERDICT DW-MRI Using Convolutional Neural Networks

Chiou, E; Giganti, F; Bonet-Carne, E; Punwani, S; Kokkinos, I; Panagiotaki, E; (2018) Prostate Cancer Classification on VERDICT DW-MRI Using Convolutional Neural Networks. In: Shi, Y and Suk, H-I and Liu, M, (eds.) Machine Learning in Medical Imaging. MLMI 2018. Lecture Notes in Computer Science, vol 11046. (pp. pp. 319-327). Springer: Cham. Green open access

[img]
Preview
Text
Prostate cancer classification.pdf - Accepted version

Download (658kB) | Preview

Abstract

Currently, non-invasive imaging techniques such as magnetic resonance imaging (MRI) are emerging as powerful diagnostic tools for prostate cancer (PCa) characterization. This paper focuses on automated PCa classification on VERDICT (Vascular, Extracellular and Restricted Diffusion for Cytometry in Tumors) diffusion weighted (DW)-MRI, which is a non-invasive microstructural imaging technique that comprises a rich imaging protocol and a tissue computational model to map in vivo histological indices. The contribution of the paper is two fold. Firstly, we investigate the potential of automated, model-free PCa classification on raw VERDICT DW-MRI. Secondly, we attempt to adapt and evaluate novel fully convolutional neural networks (FCNNs) for PCa characterization. We present two neural network architectures that adapt U-Net and ResNet-18 to the PCa classification problem. We train the networks end-to-end on DW-MRI data and evaluate the diagnostic performance employing a 10-fold cross validation approach using data acquired from 103 patients. ResNet-18 outperforms U-Net with an average AUC of 86.7% . Our results show promise for the utilization of raw VERDICT DW-MRI data and FCNNs for automating the PCa diagnostic pathway.

Type: Proceedings paper
Title: Prostate Cancer Classification on VERDICT DW-MRI Using Convolutional Neural Networks
Event: Machine Learning in Medical Imaging. MLMI 2018
ISBN-13: 978-3-030-00918-2
Open access status: An open access version is available from UCL Discovery
DOI: 10.1007/978-3-030-00919-9_37
Publisher version: https://doi.org/10.1007/978-3-030-00919-9_37
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: VERDICT MRI · Prostate Cancer Classification · Convolutional Neural Networks.
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Medicine
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Medicine > Experimental and Translational Medicine
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Surgery and Interventional Sci
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Surgery and Interventional Sci > Department of Targeted Intervention
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10057943
Downloads since deposit
87Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item