Light&fast generative adversarial network for high-fidelity CT image synthesis of liver tumor

Hepatocellular carcinoma is a common disease with high mortality. Through deep learning methods to analyze HCC CT, the screening classification and prognosis model of HCC can be established, which further promotes the development of computer-aided diagnosis and treatment in the treatment of HCC. However, there are significant challenges in the actual establishment of HCC auxiliary diagnosis model due to data imbalance, especially for rare subtypes of HCC and underrepresented demographic groups. This study proposes a GAN model aimed at overcoming these obstacles and improving the accuracy of HCC auxiliary diagnosis. In order to generate liver and tumor images close to the real distribution. Firstly, we construct a new gradient transfer sampling module to improve the lack of texture details and excessive gradient transfer parameters of the deep model; Secondly, we construct an attention module with spatial and cross-channel feature extraction ability to improve the discriminator's ability to distinguish images; Finally, we design a new loss function for liver tumor imaging features to constrain the model to approach the real tumor features in iterations. In qualitative analysis, the images synthetic by our method closely resemble the real images in liver parenchyma, blood vessels, tumors, and other parts. In quantitative analysis, the optimal results of FID, PSNR, and SSIM are 75.73, 22.77, and 0.74, respectively. Furthermore, our experiments establish classification models for imbalanced data and enhanced data, resulting in an increase in accuracy rate by 21%–34%, an increase in AUC by 0.29 - 0.33, and an increase in specificity to 0.89. Our solution provides a variety of training data sources with low cost and high efficiency for the establishment of classification or prognostic models for imbalanced data.