Mehta, P;
Antonelli, M;
Ahmed, H;
Emberton, M;
Punwani, S;
Ourselin, S;
(2020)
Decision fusion of 3D convolutional neural networks to triage patients with suspected prostate cancer using volumetric biparametric MRI.
In:
Proceedings of SPIE. Medical Imaging 2020: Computer-Aided Diagnosis.
(pp. pp. 1-11).
Society of Photo-Optical Instrumentation Engineers (SPIE)
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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.
| Type: | Proceedings paper |
|---|---|
| Title: | Decision fusion of 3D convolutional neural networks to triage patients with suspected prostate cancer using volumetric biparametric MRI |
| Event: | Medical Imaging 2020: Computer-Aided Diagnosis |
| Location: | Houston (TX), USA |
| Dates: | 15th-20th February 2020 |
| ISBN-13: | 9781510633957 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1117/12.2547682 |
| Publisher version: | https://doi.org/10.1117/12.2547682 |
| Language: | English |
| Additional information: | This version is the version of record. For information on re-use, please refer to the publisher's terms and conditions. |
| Keywords: | prostate cancer, multiparametric MRI, biparametric MRI, computer-aided diagnosis, convolutional neural network, decision fusion |
| UCL classification: | UCL 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 > Department of Imaging 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 Med Phys and Biomedical Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10101213 |
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