目的使用影像组学方法构建一个影像组学标签分类模型，对肺肿瘤良恶性进行分类预测。方法分析四川大学华西医院80例怖肿瘤患者的CT影像学数据，分割肿瘤区域，提取肿瘤形状、大小、强化程度、纹理和小波变换共485个影像组学特征。利用La...目的使用影像组学方法构建一个影像组学标签分类模型，对肺肿瘤良恶性进行分类预测。方法分析四川大学华西医院80例怖肿瘤患者的CT影像学数据，分割肿瘤区域，提取肿瘤形状、大小、强化程度、纹理和小波变换共485个影像组学特征。利用Lasso算法筛选出与肿瘤良恶性鉴别最密切的组学特征，并使用Logistic回归构建诊断肿瘤良恶性的预测模型。采用受试者工作特征（receiver operating characteristic．ROC）曲线及其曲线下面积（area under curve，AUC）来评估该影像组学标签在训练集和验证集中的效能。结果选取3个影像组学特征构建出影像组学标签，具有很好的预测分类效果。训练集的AUC为0870（95％CI：0760-0978J，灵敏度为0.870，特异度为0.818；验证集的AUC为0.853（95％CI：0.717-0.989），灵敏度为0.882，特异度为0.778。结论随着CT在临床诊断中的广泛使用，真有望成为辅助检测肿瘤良恶性的非侵入手段。展开更多
Objective To explore the semi-supervised learning(SSL) algorithm for long-tail endoscopic image classification with limited annotations.Method We explored semi-supervised long-tail endoscopic image classification in H...Objective To explore the semi-supervised learning(SSL) algorithm for long-tail endoscopic image classification with limited annotations.Method We explored semi-supervised long-tail endoscopic image classification in HyperKvasir,the largest gastrointestinal public dataset with 23 diverse classes.Semi-supervised learning algorithm FixMatch was applied based on consistency regularization and pseudo-labeling.After splitting the training dataset and the test dataset at a ratio of 4:1,we sampled 20%,50%,and 100% labeled training data to test the classification with limited annotations.Results The classification performance was evaluated by micro-average and macro-average evaluation metrics,with the Mathews correlation coefficient(MCC) as the overall evaluation.SSL algorithm improved the classification performance,with MCC increasing from 0.8761 to 0.8850,from 0.8983 to 0.8994,and from 0.9075 to 0.9095 with 20%,50%,and 100% ratio of labeled training data,respectively.With a 20% ratio of labeled training data,SSL improved both the micro-average and macro-average classification performance;while for the ratio of 50% and 100%,SSL improved the micro-average performance but hurt macro-average performance.Through analyzing the confusion matrix and labeling bias in each class,we found that the pseudo-based SSL algorithm exacerbated the classifier’ s preference for the head class,resulting in improved performance in the head class and degenerated performance in the tail class.Conclusion SSL can improve the classification performance for semi-supervised long-tail endoscopic image classification,especially when the labeled data is extremely limited,which may benefit the building of assisted diagnosis systems for low-volume hospitals.However,the pseudo-labeling strategy may amplify the effect of class imbalance,which hurts the classification performance for the tail class.展开更多
文摘目的使用影像组学方法构建一个影像组学标签分类模型，对肺肿瘤良恶性进行分类预测。方法分析四川大学华西医院80例怖肿瘤患者的CT影像学数据，分割肿瘤区域，提取肿瘤形状、大小、强化程度、纹理和小波变换共485个影像组学特征。利用Lasso算法筛选出与肿瘤良恶性鉴别最密切的组学特征，并使用Logistic回归构建诊断肿瘤良恶性的预测模型。采用受试者工作特征（receiver operating characteristic．ROC）曲线及其曲线下面积（area under curve，AUC）来评估该影像组学标签在训练集和验证集中的效能。结果选取3个影像组学特征构建出影像组学标签，具有很好的预测分类效果。训练集的AUC为0870（95％CI：0760-0978J，灵敏度为0.870，特异度为0.818；验证集的AUC为0.853（95％CI：0.717-0.989），灵敏度为0.882，特异度为0.778。结论随着CT在临床诊断中的广泛使用，真有望成为辅助检测肿瘤良恶性的非侵入手段。
文摘Objective To explore the semi-supervised learning(SSL) algorithm for long-tail endoscopic image classification with limited annotations.Method We explored semi-supervised long-tail endoscopic image classification in HyperKvasir,the largest gastrointestinal public dataset with 23 diverse classes.Semi-supervised learning algorithm FixMatch was applied based on consistency regularization and pseudo-labeling.After splitting the training dataset and the test dataset at a ratio of 4:1,we sampled 20%,50%,and 100% labeled training data to test the classification with limited annotations.Results The classification performance was evaluated by micro-average and macro-average evaluation metrics,with the Mathews correlation coefficient(MCC) as the overall evaluation.SSL algorithm improved the classification performance,with MCC increasing from 0.8761 to 0.8850,from 0.8983 to 0.8994,and from 0.9075 to 0.9095 with 20%,50%,and 100% ratio of labeled training data,respectively.With a 20% ratio of labeled training data,SSL improved both the micro-average and macro-average classification performance;while for the ratio of 50% and 100%,SSL improved the micro-average performance but hurt macro-average performance.Through analyzing the confusion matrix and labeling bias in each class,we found that the pseudo-based SSL algorithm exacerbated the classifier’ s preference for the head class,resulting in improved performance in the head class and degenerated performance in the tail class.Conclusion SSL can improve the classification performance for semi-supervised long-tail endoscopic image classification,especially when the labeled data is extremely limited,which may benefit the building of assisted diagnosis systems for low-volume hospitals.However,the pseudo-labeling strategy may amplify the effect of class imbalance,which hurts the classification performance for the tail class.
