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Proceedings Paper

Prostate cancer diagnosis using deep learning with 3D multiparametric MRI
Author(s): Saifeng Liu; Huaixiu Zheng; Yesu Feng; Wei Li
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Paper Abstract

A novel deep learning architecture (XmasNet) based on convolutional neural networks was developed for the classification of prostate cancer lesions, using the 3D multiparametric MRI data provided by the PROSTATEx challenge. End-to-end training was performed for XmasNet, with data augmentation done through 3D rotation and slicing, in order to incorporate the 3D information of the lesion. XmasNet outperformed traditional machine learning models based on engineered features, for both train and test data. For the test data, XmasNet outperformed 69 methods from 33 participating groups and achieved the second highest AUC (0.84) in the PROSTATEx challenge. This study shows the great potential of deep learning for cancer imaging.

Paper Details

Date Published: 3 March 2017
PDF: 4 pages
Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 1013428 (3 March 2017); doi: 10.1117/12.2277121
Show Author Affiliations
Saifeng Liu, The MRI Institute for Biomedical Research (Canada)
Huaixiu Zheng, Uber Technologies, Inc. (United States)
Yesu Feng, LinkedIn Corp. (United States)
Wei Li, USAA (United States)


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

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