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A 2.5D Deep Learning-Based Approach for Prostate Cancer Detection on T2-Weighted Magnetic Resonance Imaging

  • Ruba AlkadiEmail author
  • Ayman El-Baz
  • Fatma Taher
  • Naoufel Werghi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11132)

Abstract

In this paper, we propose a fully automatic magnetic resonance image (MRI)-based computer aided diagnosis (CAD) system which simultaneously performs both prostate segmentation and prostate cancer diagnosis. The system utilizes a deep-learning approach to extract high-level features from raw T2-weighted MR volumes. Features are then remapped to the original input to assign a predicted label to each pixel. In the same context, we propose a 2.5D approach which exploits 3D spatial information without a compromise in computational cost. The system is evaluated on a public dataset. Preliminary results demonstrate that our approach outperforms current state-of-the-art in both prostate segmentation and cancer diagnosis.

Notes

Acknowledgement

This work is supported by a research grant from Al-Jalila foundation Ref: AJF-201616.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Ruba Alkadi
    • 1
    Email author
  • Ayman El-Baz
    • 2
  • Fatma Taher
    • 1
  • Naoufel Werghi
    • 1
  1. 1.Khalifa University of Science and TechnologyAbu DhabiUAE
  2. 2.Department of BioengineeringUniversity of LouisvilleLouisvilleUSA

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