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A Comprehensive Study of Data Augmentation Strategies for Prostate Cancer Detection in Diffusion-Weighted MRI Using Convolutional Neural Networks

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Abstract

Data augmentation refers to a group of techniques whose goal is to battle limited amount of available data to improve model generalization and push sample distribution toward the true distribution. While different augmentation strategies and their combinations have been investigated for various computer vision tasks in the context of deep learning, a specific work in the domain of medical imaging is rare and to the best of our knowledge, there has been no dedicated work on exploring the effects of various augmentation methods on the performance of deep learning models in prostate cancer detection. In this work, we have statically applied five most frequently used augmentation techniques (random rotation, horizontal flip, vertical flip, random crop, and translation) to prostate diffusion-weighted magnetic resonance imaging training dataset of 217 patients separately and evaluated the effect of each method on the accuracy of prostate cancer detection. The augmentation algorithms were applied independently to each data channel and a shallow as well as a deep convolutional neural network (CNN) was trained on the five augmented sets separately. We used area under receiver operating characteristic (ROC) curve (AUC) to evaluate the performance of the trained CNNs on a separate test set of 95 patients, using a validation set of 102 patients for finetuning. The shallow network outperformed the deep network with the best 2D slice-based AUC of 0.85 obtained by the rotation method.

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Funding

This study received funding support in part by the Ontario Institute for Cancer Research, China Scholarship Council, and Chair in Medical Imaging and Artificial Intelligence, a joint Hospital-University Chair between the University of Toronto, The Hospital for Sick Children, and the SickKids Foundation.

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Authors and Affiliations

  1. School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, China

    Ruqian Hao & Lin Liu

  2. Institute of Medical Science, University of Toronto, Toronto, ON, Canada

    Ruqian Hao & Farzad Khalvati

  3. Department of Diagnostic Imaging, The Hospital for Sick Children (SickKids), University of Toronto, 555 University Avenue, Toronto, ON, Canada

    Ruqian Hao, Khashayar Namdar & Farzad Khalvati

  4. Joint Department of Medical Imaging, Sinai Health System, University Health Network, University of Toronto, Toronto, ON, Canada

    Masoom A. Haider

  5. Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON, Canada

    Masoom A. Haider

  6. Sunnybrook Research Institute, Toronto, ON, Canada

    Masoom A. Haider

  7. Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON, Canada

    Farzad Khalvati

Authors
  1. Ruqian Hao
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  2. Khashayar Namdar
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  3. Lin Liu
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  4. Masoom A. Haider
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  5. Farzad Khalvati
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Correspondence to Farzad Khalvati.

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Hao, R., Namdar, K., Liu, L. et al. A Comprehensive Study of Data Augmentation Strategies for Prostate Cancer Detection in Diffusion-Weighted MRI Using Convolutional Neural Networks. J Digit Imaging 34, 862–876 (2021). https://doi.org/10.1007/s10278-021-00478-7

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  • DOI: https://doi.org/10.1007/s10278-021-00478-7

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