机译:通过组合深度卷积神经网络和手工特征来预测肺结节恶性肿瘤
Southern Med Univ Sch Biomed Engn Guangdong Prov Key Lab Med Image Proc Guangzhou 510515;
Longgang Dist Peoples Hosp Shenzhen 518172 Peoples R China;
Southern Med Univ Sch Biomed Engn Guangdong Prov Key Lab Med Image Proc Guangzhou 510515;
Univ Texas Southwestern Med Ctr Dallas Dept Radiat Oncol Dallas TX 75235 USA;
Univ Texas Southwestern Med Ctr Dallas Dept Radiat Oncol Dallas TX 75235 USA;
Xidian Univ Sch Comp Sci &
Technol Xian 710071 Shaanxi Peoples R China;
Southern Med Univ Sch Biomed Engn Guangdong Prov Key Lab Med Image Proc Guangzhou 510515;
Univ Texas Southwestern Med Ctr Dallas Dept Radiat Oncol Dallas TX 75235 USA;
Southern Med Univ Sch Biomed Engn Guangdong Prov Key Lab Med Image Proc Guangzhou 510515;
Southern Med Univ Sch Tradit Chinese Med Guangzhou 510515 Guangdong Peoples R China;
Univ Texas Southwestern Med Ctr Dallas Dept Radiat Oncol Dallas TX 75235 USA;
Univ Texas Southwestern Med Ctr Dallas Dept Radiat Oncol Dallas TX 75235 USA;
lung nodule malignancy; convolutional neural network; handcrafted feature; fusion algorithm; radiomics;
机译:通过组合深度卷积神经网络和手工特征来预测肺结节恶性肿瘤
机译:用于肺结节恶性分类的可解释的深层次语义卷积神经网络
机译:一种可解释的深层分层语义卷积神经网络,用于肺结节恶性分类
机译:自动检测肺结节:使用卷积神经网络和手工制作的特征减少假阳性
机译:肺结结使用卷积神经网络分类
机译:结合手工和卷积神经网络功能检测乳腺癌病理图像中的有丝分裂
机译:一种可解释的深层分层语义卷积神经网络,用于肺结节恶性分类