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5 校准曲线和决策曲线. (a) RAD、DL、RAD_DL和Clinical_RAD_DL特征模型的校准曲线. 横坐标表示PCN分类模型的预测概率（PCN-predicted Probability），纵坐标表示实际概率（Observed Probability）；(b) RAD、DL、RAD_DL和Clinical_RAD_DL特征模型的决策曲线. 横坐标表示高风险阈值（High Risk Threshold），纵坐标表示模型的净收益（Net Benefit），All曲线表示全部预测成MCN的净收益，None曲线表示全部预测成SCN的净收益

Fig. 5 Calibration curves and decision curves. (a) Calibration curves of RAD, DL, RAD_DL, and Clinical-RAD_DL feature model. The abscissa represents the PCN-predicted probability of PCN classification model, and the ordinate represents the observed probability; (b) Decision curves used by RAD, DL, RAD_DL, and Clinical-RAD_DL feature model. The abscissa represents the high risk threshold, and the ordinate represents the net benefit of the model, the All curve represents the net benefit predicted as MCN, and the None curve represents the net benefit predicted as SCN