机译:RDA-UNET-WN:使用Wasserstein生成的对抗网络进行准确的乳房超声病变分割
Research Intern Key Laboratory of Digital Signal and Image Processing of Guangdong Province Department of Electronics Engineering College of Engineering Shantou University Shantou 515063 China;
Key Laboratory of Digital Signal and Image Processing of Guangdong Province Department of Electronics Engineering College of Engineering Shantou University Shantou 515063 China;
School of Electrical Engineering Vellore Institute of Technology Vellore India;
Key Laboratory of Digital Signal and Image Processing of Guangdong Province Department of Electronics Engineering College of Engineering Shantou University Shantou 515063 China;
Department of Electronics and Communication Engineering Kuwait College of Science and Technology Doha 13133 Kuwait;
Breast Ultra Sound (BUS)images; Residual-Dilated-Attention-Gate-UNet (RDAU-NET); Generative Adversarial Network (GAN); Tumor segmentation;
机译:乳房超声图像与注意力生成对抗网络的半监督分段
机译:用生成的对抗网络将视网膜和视网膜血管和光盘分割的精确分割
机译:基于标签分配生成对抗网络的CT扫描的准确结直肠肿瘤分割
机译:用深卷积生成的对抗网络改善皮肤病变分割
机译:各种规范对Wasserstein生成对抗网络的影响的比较评估
机译:利用生殖对抗网络实现眼底图像中视网膜血管和视盘的精确分割
机译:使用半像素 - 明智的循环发生越野网超声波的病变分割