基于LSTM与注意力结构的肺结节多特征抽取方法

倪扬帆, 杨媛媛, 谢哲, 郑德重, 王卫东

Multi-Feature Extraction of Pulmonary Nodules Based on LSTM and Attention Structure

NI Yangfan, YANG Yuanyuan, XIE Zhe, ZHENG Dezhong, WANG Weidong

表2 各模型仅使用结节CT图像进行多任务分类的结果比较

Tab.2 Results of different multi-task classification models taking nodule CT images as input

分类特征 Res50 OMoE Res50+LSTM Res50+ATT+LSTM θ% F1% e θ% F1% e θ% F1% e θ% F1% e 形状 66.89 45.25 0.79 67.22 46.52 0.82 68.24 48.61 0.75 70.01 50.97 0.69 边界 78.93 87.12 0.78 78.26 87.22 0.85 78.95 88.64 0.76 79.21 88.01 0.80 毛刺 94.98 97.26 0.58 93.97 96.89 0.57 94.31 97.86 0.53 94.32 97.38 0.53 分叶 94.98 97.42 0.67 94.98 97.42 0.68 95.00 97.46 0.62 95.55 97.89 0.52 内部成分 99.33 34.53 0.03 99.33 34.44 0.02 99.33 34.81 0.07 99.32 33.59 0.05 实性程度 68.56 73.30 0.59 68.22 74.09 0.64 75.57 75.67 0.57 78.62 71.94 0.48 钙化 88.63 94.55 0.32 84.61 91.84 0.41 89.97 94.71 0.35 90.00 94.93 0.33 恶性程度 83.27 66.89 0.77 79.60 66.92 0.90 84.60 67.95 0.70 85.10 75.98 0.63