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

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Image Analysis and Recognition (ICIAR 2017)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10317))

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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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Notes

  1. 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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Author information

Authors and Affiliations

  1. Department of Computer Science, University of Waterloo, Waterloo, Canada

    Amir-Hossein Karimi

  2. Department of Systems Design Engineering, University of Waterloo, Waterloo, Canada

    Audrey G. Chung, Mohammad Javad Shafiee & Alexander Wong

  3. Department of Statistics and Acturial Sciences, University of Waterloo, Waterloo, Canada

    Ali Ghodsi

  4. Department of Medical Imaging, University of Toronto, Toronto, Canada

    Farzad Khalvati & Masoom A. Haider

Authors
  1. Amir-Hossein Karimi
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  2. Audrey G. Chung
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  3. Mohammad Javad Shafiee
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  4. Farzad Khalvati
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  5. Masoom A. Haider
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  6. Ali Ghodsi
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  7. Alexander Wong
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Corresponding author

Correspondence to Amir-Hossein Karimi .

Editor information

Editors and Affiliations

  1. University of Waterloo, Waterloo, ON, Canada

    Fakhri Karray

  2. University of Porto, Porto, Portugal

    Aurélio Campilho

  3. Politechnique Montreal, Montreal, QC, Canada

    Farida Cheriet

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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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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59875-8

  • Online ISBN: 978-3-319-59876-5

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