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.