Prostate cancer diagnosis using deep learning with 3D multiparametric MRI

Saifeng Liu; Huaixiu Zheng; Yesu Feng; Wei Li

doi:10.1117/12.2277121

3 March 2017 Prostate cancer diagnosis using deep learning with 3D multiparametric MRI

Saifeng Liu, Huaixiu Zheng, Yesu Feng, Wei Li

Proceedings Volume 10134, Medical Imaging 2017: Computer-Aided Diagnosis; 1013428 (2017) https://doi.org/10.1117/12.2277121
Event: SPIE Medical Imaging, 2017, Orlando, Florida, United States

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.

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Citation Download Citation

Saifeng Liu, Huaixiu Zheng, Yesu Feng, and Wei Li "Prostate cancer diagnosis using deep learning with 3D multiparametric MRI", Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 1013428 (3 March 2017); https://doi.org/10.1117/12.2277121

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