Discovery Radiomics via a Mixture of Deep ConvNet Sequencers for Multi-parametric MRI Prostate Cancer Classification

Prostate cancer is the most diagnosed form of cancer in men, but prognosis is relatively good with a sufficiently early diagnosis. Radiomics has been shown to be a powerful prognostic tool for cancer detection; however, these radiomics-driven methods currently rely on hand-crafted sets of quantitative imaging-based features, which can limit their ability to fully characterize unique prostate cancer tumour traits. We present a novel discovery radiomics framework via a mixture of deep convolutional neural network (ConvNet) sequencers for generating custom radiomic sequences tailored for prostate cancer detection. We evaluate the performance of the mixture of ConvNet sequencers against state-of-the-art hand-crafted radiomic sequencers for binary computer aided prostate cancer classification using real clinical prostate multi parametric MRI data. Results for the mixture of ConvNet sequencers demonstrate good performance in prostate cancer classification relative to the hand-crafted radiomic sequencers, and show potential for more efficient and reliable automatic prostate cancer classification.