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.
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Jarrel C. Y. Seah, Jennifer S. N. Tang, Andy Kitchen, "Detection of prostate cancer on multiparametric MRI," Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 1013429 (3 March 2017);