KDE-GAN: A multimodal medical image-fusion model based on knowledge distillation and explainable AI modules.

Journal: Computers in biology and medicine
Published Date: Nov 3, 2022

Abstract

BACKGROUND: As medical images contain sensitive patient information, finding a publicly accessible dataset with patient permission is challenging. Furthermore, few large-scale datasets suitable for training image-fusion models are available. To address this issue, we propose a medical image-fusion model based on knowledge distillation (KD) and an explainable AI module-based generative adversarial network with dual discriminators (KDE-GAN).

Authors

  • Jia Mi
    Department of Special Examination, Shandong Provincial Third Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250031, China.
  • Lifang Wang
    School of Chemistry and Chemical Engineering, South China University of Technology, Guangzhou 510640, PR China. Electronic address: lifangwang@139.com.
  • Yang Liu
    Department of Computer Science, Hong Kong Baptist University, Hong Kong, China.
  • Jiong Zhang
    Laboratory of Advanced Theranostic Materials and Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, Zhejiang, China.

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