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Proceedings Paper

Decision fusion of 3D convolutional neural networks to triage patients with suspected prostate cancer using volumetric biparametric MRI
Author(s): Pritesh Mehta; Michela Antonelli; Hashim Ahmed; Mark Emberton; Shonit Punwani; Sebastien Ourselin
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Paper Abstract

In this work, we present a computer-aided diagnosis system that uses deep learning and decision fusion to classify patients into one of three classes: “Likely Prostate Cancer," “Equivocal" and “Likely not Prostate Cancer." We impose the group “Equivocal" to reduce misclassifications by allowing for uncertainty, akin to prostate imaging reporting systems used by radiologists. We trained 3D convolutional neural networks to perform two binary patient-level classification tasks: classification of patients with/without prostate cancer and classification of patients with/without clinically significant prostate cancer. Networks were trained separately using volumetric T2-weighted images and apparent diffusion coefficient maps for both tasks. The probabilistic outputs of the resulting four trained networks were combined using majority voting followed by the max operator to classify patients into one of the three classes mentioned above. All networks were trained using patient-level labels only, which is a key advantage of our system since voxel-level tumour annotation is often unavailable due to the time and effort required of a radiologist. Our system was evaluated by retrospective analysis on a previously collected trial dataset. At a higher sensitivity setting, our system achieved 0.97 sensitivity and 0.31 specificity compared to an experienced radiologist who achieved 0.99 sensitivity and 0.12 specificity. At a lower sensitivity setting, our system achieved 0.78 sensitivity and 0.77 specificity compared to 0.76 sensitivity and 0.77 specificity for the experienced radiologist. We envision our system acting as a second reader in pre-biopsy screening applications.

Paper Details

Date Published: 16 March 2020
PDF: 11 pages
Proc. SPIE 11314, Medical Imaging 2020: Computer-Aided Diagnosis, 1131433 (16 March 2020); doi: 10.1117/12.2547682
Show Author Affiliations
Pritesh Mehta, Univ. College London (United Kingdom)
Michela Antonelli, King's College London (United Kingdom)
Hashim Ahmed, Imperial College London (United Kingdom)
Mark Emberton, Univ. College London (United Kingdom)
Shonit Punwani, Univ. College London (United Kingdom)
Sebastien Ourselin, King's College London (United Kingdom)

Published in SPIE Proceedings Vol. 11314:
Medical Imaging 2020: Computer-Aided Diagnosis
Horst K. Hahn; Maciej A. Mazurowski, Editor(s)

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