基于深度学习的胰腺黏液性和浆液性囊性肿瘤的多源特征分类模型

徐真顺,袁小涵,黄子珩,邵成伟,武杰,边云

Multi-source Feature Classification Model of Pancreatic Mucinous and Serous Cystic Neoplasms Based on Deep Learning

XU Zhenshun,YUAN Xiaohan,HUANG Ziheng,SHAO Chengwei,WU Jie,BIAN Yun

图3 (a)对影像组学特征做筛选得到的LASSO模型MSE图.通过调整λ值使MSE达到最小，以确定最佳λ值；(b)对影像组学特征进行筛选的最优特征系数收敛图；(c) LASSO筛选的影像组学特征权重图，Skewness.1表示偏度，Busyness.1表示复杂度，MCC.2表示形态学相关系数，DependenceVariance.2表示依赖性差异度，Idn.4表示逆差分矩，Correlattion.7表示相关性；(d)对深度学习特征做筛选得到的LASSO模型MSE图；(e)对深度学习特征进行筛选的最优特征系数收敛图；(f) LASSO筛选的深度学习特征权重图

Fig. 3 (a) Mean Square Error (MSE) graphs corresponding to different λ values of radiomics features. The MSE is minimized by adjusting the λ value to determine the optimal λ value; (b) Convergence diagram of the optimal characteristic coefficient of radiomics features; (c) Weight of radiomics features screened by LASSO, Skewness.1 represents skewness, Busyness.1 represents complexity, MCC.2 represents morphological correlation coefficient, DependencyVariance.2 represents dependency difference, Idn.4 represents inverse difference moment, and Correlation.7 represents correlation; (d) Mean square error (MSE) graphs corresponding to different λ values of deep learning features; (e) Convergence diagram of the optimal characteristic coefficient of deep learning features; (f) Weight of deep learning features screened by LASSO