Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany;
Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany;
Department of Radiology, DKFZ, Heidelberg, Germany;
Department of Radiology, DKFZ, Heidelberg, Germany,Institute of Radiology, University Hospital Erlangen, Erlangen, Germany;
Radiological Practice at the ATOS Clinic, Heidelberg, Germany;
Radiology Center Mannheim (RZM), Mannheim, Germany;
Medical Physics in Radiology, DKFZ, Heidelberg, Germany;
Department of Radiology, DKFZ, Heidelberg, Germany;
Department of Radiology, DKFZ, Heidelberg, Germany;
Department of Radiology, DKFZ, Heidelberg, Germany;
Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany;
Convolutional neural networks Diffusion-weighted MR imaging; Deep learning; Lesion classification Domain adaptation;
机译:短暂性脑缺血发作患者的亚急性临床队列中3梯度和20梯度方向扩散加权成像的比较:标准供应商协议在病变检测和最终梗死面积预测中的应用
机译:动态增强造影和弥散加权磁共振在不同成像方案下对乳腺病变的诊断价值
机译:扩散加权成像是用于全身癌症分期的混合PET / MRI方案的一部分:它对病变检测有好处吗?
机译:域适应用于偏离基于CNN的病变分类在扩散加权MR图像中的采集协议
机译:使用DIFRAD-FSE(具有快速自旋回波的扩散加权径向采集)MRI的高分辨率扩散成像。
机译:短暂性脑缺血发作患者临床亚急性队列中3梯度和20梯度方向扩散加权成像的比较:标准供应商协议在病变检测和最终梗死面积预测中的应用
机译:短暂性脑缺血发作患者临床亚急性队列中3和20梯度方向扩散加权成像的比较:标准供应商协议在病变检测和最终梗塞面积预测中的应用