A High Magnification Cell Micro-image Generative Adversarial Network

• Published in: Jisuanji gongcheng
• Main Author: MIAO Qiaowei, YANG Qi, LI Aijia, LUO Wenjie
• Format: Article
• Language: English
• Published: Editorial Office of Computer Engineering 2020-06-01
• Subjects: cyclegan network|generative adversarial network(gan)|adversarial learning|convolutional neural network(cnn)|deep learning
• Online Access: https://www.ecice06.com/fileup/1000-3428/PDF/20200636.pdf
• ISSN: 1000-3428

In terms of medical treatment,the diagnosis of many diseases relies on the observation of microscopic objects such as cells with a high magnification microscope.However,due to the high price and complex operation of high magnification microscope and there are some problems in the reconstruction of high magnification cell micro-images,such as the inconsistency of image style between high magnification micro-images and low magnification micro-images,the different resolution of cell images and the lacking of paired training data.To solve the above problems,a high magnification cell micro-images generative adversarial network is proposed.Based on the CycleGAN,a new residual dense block is added to the generator while the new activation function is introduced,and the Batch Normalization(BN) layers are removed.At the same time,in order to ensure the authenticity of the generated images,the detail perceptual loss is introduced to the training process of the generator.Experimental results show that the proposed method can effectively restore the detail of the high magnification micro-images while preserving the basic information of the low magnification micro-images.