Medical artificial intelligence(AI)and big data technology have rapidly advanced in recent years,and they are now routinely used for image-based diagnosis.China has a massive amount of medical data.However,a uniform c...Medical artificial intelligence(AI)and big data technology have rapidly advanced in recent years,and they are now routinely used for image-based diagnosis.China has a massive amount of medical data.However,a uniform criteria for medical data quality have yet to be established.Therefore,this review aimed to develop a standardized and detailed set of quality criteria for medical data collection,storage,annotation,and management related to medical AI.This would greatly improve the process of medical data resource sharing and the use of AI in clinical medicine.展开更多
Deep learning has led to tremendous success in machine maintenance and fault diagnosis.However,this success is predicated on the correctly annotated datasets.Labels in large industrial datasets can be noisy and thus d...Deep learning has led to tremendous success in machine maintenance and fault diagnosis.However,this success is predicated on the correctly annotated datasets.Labels in large industrial datasets can be noisy and thus degrade the performance of fault diagnosis models.The emerging concept of broad learning shows the potential to address the label noise problem.Compared with existing deep learning algorithms,broad learning has a simple architecture and high training efficiency.An active label denoising algorithm based on broad learning(ALDBL)is proposed.First,ALDBL captures the embedded representation from the time-frequency features by a recurrent memory cell.Second,it augments wide features with a sparse autoencoder and projects the sparse features into an orthogonal space.A proposed corrector then iteratively changes the weights of source examples during the training and corrects the labels by using a label adaptation matrix.Finally,ALDBL finetunes the model parameters with actively sampled target data with reliable pseudo labels.The performance of ALDBL is validated with three benchmark datasets,including 30 label denoising tasks.Computational results demonstrate the effectiveness and advantages of the proposed algorithm over the other label denoising algorithms.展开更多
基金supported by the Science and Technology Planning Projects of Guangdong Province(Grant No.2018B010109008)Na-tional Key R&D Program of China(Grant No.2018YFC0116500).
文摘Medical artificial intelligence(AI)and big data technology have rapidly advanced in recent years,and they are now routinely used for image-based diagnosis.China has a massive amount of medical data.However,a uniform criteria for medical data quality have yet to be established.Therefore,this review aimed to develop a standardized and detailed set of quality criteria for medical data collection,storage,annotation,and management related to medical AI.This would greatly improve the process of medical data resource sharing and the use of AI in clinical medicine.
基金supported by the China Scholarship Council during a research visit of Guokai Liu to the University of Iowa(Grant No.201906160078)the Fundamental Research Funds for the Central Universities(Grant No.HUST:2021GCRC058)。
文摘Deep learning has led to tremendous success in machine maintenance and fault diagnosis.However,this success is predicated on the correctly annotated datasets.Labels in large industrial datasets can be noisy and thus degrade the performance of fault diagnosis models.The emerging concept of broad learning shows the potential to address the label noise problem.Compared with existing deep learning algorithms,broad learning has a simple architecture and high training efficiency.An active label denoising algorithm based on broad learning(ALDBL)is proposed.First,ALDBL captures the embedded representation from the time-frequency features by a recurrent memory cell.Second,it augments wide features with a sparse autoencoder and projects the sparse features into an orthogonal space.A proposed corrector then iteratively changes the weights of source examples during the training and corrects the labels by using a label adaptation matrix.Finally,ALDBL finetunes the model parameters with actively sampled target data with reliable pseudo labels.The performance of ALDBL is validated with three benchmark datasets,including 30 label denoising tasks.Computational results demonstrate the effectiveness and advantages of the proposed algorithm over the other label denoising algorithms.
