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Joint Detection and Diagnosis of Prostate Cancer in Multi-parametric MRI Based on Multimodal Convolutional Neural Networks

  • Xin YangEmail author
  • Zhiwei Wang
  • Chaoyue Liu
  • Hung Minh Le
  • Jingyu Chen
  • Kwang-Ting (Tim) Cheng
  • Liang WangEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10435)

Abstract

This paper presents an automated method for jointly localizing prostate cancer (PCa) in multi-parametric MRI (mp-MRI) images and assessing the aggressiveness of detected lesions. Our method employs multimodal multi-label convolutional neural networks (CNNs), which are trained in a weakly-supervised manner by providing a set of prostate images with image-level labels without priors of lesions’ locations. By distinguishing images with different labels, discriminative visual patterns related to indolent PCa and clinically significant (CS) PCa are automatically learned from clutters of prostate tissues. Cancer response maps (CRMs) with each pixel indicating the likelihood of being part of indolent/CS are explicitly generated at the last convolutional layer. We define new back-propagate error of CNN to enforce both optimized classification results and consistent CRMs for different modalities. Our method enables the feature learning processes of different modalities to mutually influence each other and, in turn yield more representative features. Comprehensive evaluation based on 402 lesions demonstrates superior performance of our method to the state-of-the-art method [13].

Keywords

Prostate cancer detection and diagnosis Convolutional neural network Multimodal fusion 

Notes

Acknowledgment

This work is funded by National Natural Science Foundation of China: 61502188.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Xin Yang
    • 1
    Email author
  • Zhiwei Wang
    • 1
  • Chaoyue Liu
    • 1
  • Hung Minh Le
    • 1
  • Jingyu Chen
    • 1
  • Kwang-Ting (Tim) Cheng
    • 2
  • Liang Wang
    • 3
    Email author
  1. 1.School of EICHUSTWuhanChina
  2. 2.School of EngineeringHKUSTKowloonHong Kong
  3. 3.Department of Radiology, Tongji HospitalHUSTWuhanChina

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