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机译:用深层多任务学习整合肺实质分割和结节检测
Beijing Inst Technol Sch Comp Sci Beijing Lab Intelligent Informat Beijing 100081 Peoples R China;
Beijing Inst Technol Sch Comp Sci Beijing Lab Intelligent Informat Beijing 100081 Peoples R China;
Beijing Inst Technol Sch Comp Sci Beijing Lab Intelligent Informat Beijing 100081 Peoples R China;
Brunel Univ London Dept Comp Sci Uxbridge UB8 3PH Middx England;
Acad Med Sci Canc Hosp Dept Imaging Diag Chinese Beijing 100021 Peoples R China;
Acad Med Sci Canc Hosp Dept Imaging Diag Chinese Beijing 100021 Peoples R China;
Lung; Image segmentation; Convolution; Computed tomography; Task analysis; Feature extraction; Three-dimensional displays; Deep convolutional networks; lung nodule detection; lung paranchyma segmentation; multi-tasking learning;
机译:集成域名知识在培训多任务级联深度学习模型中对超声图像的良性恶性甲状腺结节分类
机译:肺结结3D分割的多视图二次输入协作深度学习
机译:边缘结节丢失的多任务深度模型
机译:来自CT的肺癌早期检测图像:深度学习结节分割和分类
机译:通过深度强化学习检测肺结节
机译:使用多任务学习和深射辐射术语预测肺腺癌的侵袭性肺腺癌作为地面玻璃结节
机译:肺深:一种用于检测使用深度学习算法检测肺结节图案的肺结核模式的计算机化工具
机译:提高CT诊断特异性早期发现肺癌:基于4D CT的肺结节弹性测量。