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Computer-aided diagnosis based on image color rendering promotes medical image analysis and doctor-patient communication by highlighting important information of medical diagnosis. To overcome the limitations of the color rendering method based on deep learning, such as poor model stability, poor rendering quality, fuzzy boundaries and crossed color boundaries, we propose a novel hinge-cross-entropy generative adversarial network (HCEGAN). The self-attention mechanism was added and improved to focus on the important information of the image. And the hinge-cross-entropy loss function was used to stabilize the training process of GAN models. In this study, we implement the HCEGAN model for image color rendering based on DIV2 K and COCO datasets, and evaluate the results using SSIM and PSNR. The experimental results show that the proposed HCEGAN automatically re-renders images, significantly improves the quality of color rendering and greatly improves the stability of prior GAN models.

Image Color Rendering Based on Hinge-Cross-Entropy GAN in Internet of Medical Things