Laser & Optoelectronics Progress, Volume. 59, Issue 22, 2215005(2022)

Medical Image Fusion Based on Semisupervised Learning and Generative Adversarial Network

Haitao Yin* and Yongying Yue
Author Affiliations
  • College of Automation & College of Artificial Intelligence, Nanjing University of Posts and Telecommunications, Nanjing 210023, Jiangsu , China
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    To efficiently employ a small amount of labeled data, a medical image fusion network based on semisupervised learning and a generative adversarial network is developed. The developed fusion network comprises one generator and two discriminators. A semisupervised learning scheme is developed to train the network, including the supervised-training, unsupervised training, and parameters fine-tuning phases. Furthermore, the generator is constructed using a fusion inspired U-Net, squeeze and excitation attention modules. The discriminator contains three convolution layers, one fully connected layer, and a sigmoid activation function. The experimental findings on different multimodal medical images exhibit the proposed approach is competitive with six existing deep-learning based approaches in terms of visual effects and objective indexes. Moreover, the ablation investigations show the effectiveness of a semisupervised learning scheme that can enhance the quality of fused images.

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    Haitao Yin, Yongying Yue. Medical Image Fusion Based on Semisupervised Learning and Generative Adversarial Network[J]. Laser & Optoelectronics Progress, 2022, 59(22): 2215005

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    Paper Information

    Category: Machine Vision

    Received: Aug. 12, 2021

    Accepted: Oct. 13, 2021

    Published Online: Sep. 23, 2022

    The Author Email: Yin Haitao (haitaoyin@njupt.edu.cn)

    DOI:10.3788/LOP202259.2215005

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