National Institutes of Health Clinical Center, Bethesda, MD 20892, USA;
National Institutes of Health Clinical Center, Bethesda, MD 20892, USA,University of Florida, Gainesville, FL 32611, USA;
National Institutes of Health Clinical Center, Bethesda, MD 20892, USA;
National Institutes of Health Clinical Center, Bethesda, MD 20892, USA;
National Institutes of Health Clinical Center, Bethesda, MD 20892, USA;
Ping An Insurance Company of China, Shenzhen 510852, China;
University of Florida, Gainesville, FL 32611, USA;
National Institutes of Health Clinical Center, Bethesda, MD 20892, USA;
CT image enhancement; Lesion segmentation; Stacked generative adversarial networks; Transfer learning;
机译:基于对比度增强条件生成对抗网络和转移学习的基于对比度射线照相图像焊接缺陷检测
机译:基于多源遥感图像分割和对象检测的基于生成的对抗网络级域传输的基于生成的对抗网络传输
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:CT图像增强使用堆叠生成的对抗网络和转移学习对病变分割改进
机译:用于学习图像分割图的其他特征的堆叠生成的对抗网络
机译:通过增强具有生成对抗性网络的胶质瘤患者的数据来改进多射门MR图像分割
机译:CT图像增强使用堆叠生成的对抗网络和转移学习对病变分割改进