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.
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 (Presented at SPIE Medical Imaging: 3 March 2017; Published: 3 March 2017); https://doi.org/10.1117/12.2277121.
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