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Proceedings Paper

Detection of prostate cancer on multiparametric MRI
Author(s): Jarrel C. Y. Seah; Jennifer S. N. Tang; Andy Kitchen
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Paper Abstract

In this manuscript, we describe our approach and methods to the ProstateX challenge, which achieved an overall AUC of 0.84 and the runner-up position. We train a deep convolutional neural network to classify lesions marked on multiparametric MRI of the prostate as clinically significant or not.

We implement a novel addition to the standard convolutional architecture described as auto-windowing which is clinically inspired and designed to overcome some of the difficulties faced in MRI interpretation, where high dynamic ranges and low contrast edges may cause difficulty for traditional convolutional neural networks trained on high contrast natural imagery. We demonstrate that this system can be trained end to end and outperforms a similar architecture without such additions. Although a relatively small training set was provided, we use extensive data augmentation to prevent overfitting and transfer learning to improve convergence speed, showing that deep convolutional neural networks can be feasibly trained on small datasets.

Paper Details

Date Published: 3 March 2017
PDF: 4 pages
Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 1013429 (3 March 2017); doi: 10.1117/12.2277122
Show Author Affiliations
Jarrel C. Y. Seah, Alfred Health (Australia)
STAT Innovations Pty. Ltd. (Australia)
Jennifer S. N. Tang, Melbourne Health (Australia)
STAT Innovations Pty. Ltd. (Australia)
Andy Kitchen, Silverpond Pty. Ltd. (Australia)

Published in SPIE Proceedings Vol. 10134:
Medical Imaging 2017: Computer-Aided Diagnosis
Samuel G. Armato III; Nicholas A. Petrick, Editor(s)

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