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机译:特征共享自适应 - 促进CT图像中肺结血结节侵袭性分类的深度学习
School of Biomedical EngineeringShanghai Jiao Tong UniversityShanghai 200240 China;
Medical Imaging DepartmentJinhua Municipal Central HospitalJinhua 321001 China;
College of Computer Science and TechnologyZhejiang UniversityHangzhou 310027 China;
School of Biomedical EngineeringShanghai Jiao Tong UniversityShanghai 200240 China;
Medical Imaging DepartmentJinhua Municipal Central HospitalJinhua 321001 China;
Changzhou Industrial Technology Research Institute of Zhejiang UniversityChangzhou 213022 China;
Medical Imaging DepartmentJinhua Municipal Central HospitalJinhua 321001 China;
School of Biomedical EngineeringShanghai Jiao Tong UniversityShanghai 200240 China;
computed tomography; deep learning; invasiveness classification; pulmonary nodule;
机译:特征共享自适应 - 促进CT图像中肺结血结节侵袭性分类的深度学习
机译:基于CT的深度学习模型,以区分侵入性肺腺癌出现作为外科候选者的子样本结节:基于尺寸的物流模型和放射科学家的诊断性能比较
机译:T1大小外周肺腺癌表现为子弹结节的预测性CT特征
机译:深度特征学习用于肺部CT的肺结节分类
机译:CT上的肺结节:机器学习,用于开发和评估与恶性状态有关的图像特征。
机译:利用深学习预测CT图像对CT图像的恶性和侵袭性
机译:肺结血结节的定性和定量成像特征:区分侵入性腺癌免受微创腺癌和浸润性损伤的侵袭性腺癌