机译:使用3-D深泄漏Noisy-OR网络评估肺结节的恶性程度
Tsinghua Univ Sch Med Beijing 100084 Peoples R China|Shukun Network Technol Ltd Beijing 100020 Peoples R China;
Tsinghua Univ Inst Artificial Intelligence Beijing 100084 Peoples R China|Tsinghua Univ State Key Lab Intelligent Technol & Syst Beijing Natl Res Ctr Informat Sci & Technol Beijing 100084 Peoples R China|Tsinghua Univ Dept Comp Sci & Technol Beijing 100084 Peoples R China;
Tsinghua Univ Sch Med Beijing 100084 Peoples R China;
Cancer; Solid modeling; Noise measurement; Lung; Proposals; Task analysis; Object detection; 3-D convolutional neural network (CNN); deep learning; nodule malignancy evaluation; noisy-OR model; pulmonary nodule detection;
机译:用于胸部CT肺结节自动检测的3-D卷积神经网络
机译:肺结结性肺结结性恶性评估的卷积神经网络整合肺癌分类管道
机译:结节:将受限的多尺度LoG滤镜与密集扩张的3D深卷积神经网络相结合,以进行肺结节检测
机译:基于ARG的肺结节恶性评估
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:使用由生成对抗网络训练的深卷积神经网络在计算机断层扫描图像中自动进行肺结节分类
机译:使用由生成的对抗网络训练的深卷积神经网络在计算机断层扫描图像中自动肺结核分类