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

作　　者：Hong’an Li Min Zhang Dufeng Chen Jing Zhang Meng Yang Zhanli Li 

机构地区：[1]College of Computer Science and Technology,Xi’an University of Science and Technology,Xi’an,710054,China [2]Beijing Geotechnical and Investigation Engineering Insititute,Beijing,100080,China [3]Xi’an Institute of Applied Optics,Xi’an,710065,China

出　　处：《Computer Modeling in Engineering & Sciences》2023年第4期779-794,共16页工程与科学中的计算机建模（英文）

基　　金：Foundation of China(No.61902311)funding for this study;supported in part by the Natural Science Foundation of Shaanxi Province in China under Grants 2022JM-508,2022JM-317 and 2019JM-162.

摘　　要：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 DIV2K 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.

关 键 词：Internet of Medical Things medical image analysis image color rendering loss function self-attention generative adversarial networks 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]