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Task-GAN: Improving Generative Adversarial Network for Image Reconstruction

  • Jiahong Ouyang
  • Guanhua Wang
  • Enhao Gong
  • Kevin Chen
  • John Pauly
  • Greg ZaharchukEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11905)

Abstract

Generative Adversarial Network (GAN) has demonstrated great potentials in computer vision tasks such as image restoration. However, image restoration for specific scenarios, such as medical image enhancement is still facing challenge: How to ensure the visually plausible results while not containing hallucinated features that might jeopardize downstream tasks such as pathology identification? Here, we propose Task-GAN, a generalized model for medical reconstruction problem. A task-specific network that captures the diagnostic/pathology features, was added to couple the GAN based image reconstruction framework. Validated on multiple medical datasets, we demonstrated that the proposed method leads to improved deep learning based image reconstruction while preserving the detailed structure and diagnostic features.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Jiahong Ouyang
    • 1
  • Guanhua Wang
    • 1
    • 2
  • Enhao Gong
    • 1
    • 3
  • Kevin Chen
    • 1
  • John Pauly
    • 1
  • Greg Zaharchuk
    • 1
    • 3
    Email author
  1. 1.Stanford UniversityStanfordUSA
  2. 2.Tsinghua UniversityBeijingChina
  3. 3.Subtle MedicalMenlo ParkUSA

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