Abstract
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.
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- 1.
A total of 11,424 (i.e., \(714 \times 8 \times 2\)) augmented healthy samples were generated, of which 1280 candidates were drawn at random to balance the training set.
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Karimi, AH. et al. (2017). Discovery Radiomics via a Mixture of Deep ConvNet Sequencers for Multi-parametric MRI Prostate Cancer Classification. In: Karray, F., Campilho, A., Cheriet, F. (eds) Image Analysis and Recognition. ICIAR 2017. Lecture Notes in Computer Science(), vol 10317. Springer, Cham. https://doi.org/10.1007/978-3-319-59876-5_6
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DOI: https://doi.org/10.1007/978-3-319-59876-5_6
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