Hepatocellular carcinoma(HCC)is the sixth most common malignancy and the fourth leading cause of cancer related death worldwide.China covers over half of cases,leading HCC to be a vital threaten to public health.Despi...Hepatocellular carcinoma(HCC)is the sixth most common malignancy and the fourth leading cause of cancer related death worldwide.China covers over half of cases,leading HCC to be a vital threaten to public health.Despite advances in diagnosis and treatments,high recurrence rate remains a major obstacle in HCC management.Multi-omics currently facilitates surveillance,precise diagnosis,and personalized treatment decision making in clinical setting.Non-invasive radiomics utilizes preoperative radiological imaging to reflect subtle pixel-level pattern changes that correlate to specific clinical outcomes.Radiomics has been widely used in histopathological diagnosis prediction,treatment response evaluation,and prognosis prediction.High-throughput sequencing and gene expression profiling enabled genomics and proteomics to identify distinct transcriptomic subclasses and recurrent genetic alterations in HCC,which would reveal the complex multistep process of the pathophysiology.The accumulation of big medical data and the development of artificial intelligence techniques are providing new insights for our better understanding of the mechanism of HCC via multi-omics,and show potential to convert surgical/intervention treatment into an antitumorigenic one,which would greatly advance precision medicine in HCC management.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Hepatocellular carcinoma(HCC)is the sixth most common malignancy and the fourth leading cause of cancer related death worldwide.China covers over half of cases,leading HCC to be a vital threaten to public health.Despite advances in diagnosis and treatments,high recurrence rate remains a major obstacle in HCC management.Multi-omics currently facilitates surveillance,precise diagnosis,and personalized treatment decision making in clinical setting.Non-invasive radiomics utilizes preoperative radiological imaging to reflect subtle pixel-level pattern changes that correlate to specific clinical outcomes.Radiomics has been widely used in histopathological diagnosis prediction,treatment response evaluation,and prognosis prediction.High-throughput sequencing and gene expression profiling enabled genomics and proteomics to identify distinct transcriptomic subclasses and recurrent genetic alterations in HCC,which would reveal the complex multistep process of the pathophysiology.The accumulation of big medical data and the development of artificial intelligence techniques are providing new insights for our better understanding of the mechanism of HCC via multi-omics,and show potential to convert surgical/intervention treatment into an antitumorigenic one,which would greatly advance precision medicine in HCC management.
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.
