Funding agencies play a pivotal role in bolstering research endeavors by allocating financial resources for data collection and analysis.However,the lack of detailed information regarding the methods employed for data...Funding agencies play a pivotal role in bolstering research endeavors by allocating financial resources for data collection and analysis.However,the lack of detailed information regarding the methods employed for data gathering and analysis can obstruct the replication and utilization of the results,ultimately affecting the study’s transparency and integrity.The task of manually annotating extensive datasets demands considerable labor and financial investment,especially when it entails engaging specialized individuals.In our crowd counting study,we employed the web-based annotation tool SuperAnnotate to streamline the human annotation process for a dataset comprising 3,000 images.By integrating automated annotation tools,we realized substantial time efficiencies,as demonstrated by the remarkable achievement of 858,958 annotations.This underscores the significant contribution of such technologies to the efficiency of the annotation process.展开更多
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
Recent advancements in the field of medical artificial intelligence(AI)have led to the widespread adoption of foundational and large language models.This review paper explores their applications within medical AI,intr...Recent advancements in the field of medical artificial intelligence(AI)have led to the widespread adoption of foundational and large language models.This review paper explores their applications within medical AI,introducing a novel classification framework that categorizes them as disease-specific,general-domain,and multi-modal models.The paper also addresses key challenges such as data acquisition and augmentation,including issues related to data volume,annotation,multi-modal fusion,and privacy concerns.Additionally,it discusses the evaluation,validation,limitations,and regulation of medical AI models,emphasizing their transformative potential in healthcare.The importance of continuous improvement,data security,standardized evaluations,and collaborative approaches is highlighted to ensure the responsible and effective integration of AI into clinical applications.展开更多
基金The authors acknowledge that this research work was supported through project number UCS&T/R&D-09/19–20/17533 titled“An Intelligent Computational Model for Crowd Demonstration and Risk Analysis during Spiritual Events in Haridwar’by the Uttarakhand Council for Science and Technology(UCOST),India.”。
文摘Funding agencies play a pivotal role in bolstering research endeavors by allocating financial resources for data collection and analysis.However,the lack of detailed information regarding the methods employed for data gathering and analysis can obstruct the replication and utilization of the results,ultimately affecting the study’s transparency and integrity.The task of manually annotating extensive datasets demands considerable labor and financial investment,especially when it entails engaging specialized individuals.In our crowd counting study,we employed the web-based annotation tool SuperAnnotate to streamline the human annotation process for a dataset comprising 3,000 images.By integrating automated annotation tools,we realized substantial time efficiencies,as demonstrated by the remarkable achievement of 858,958 annotations.This underscores the significant contribution of such technologies to the efficiency of the annotation process.
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.
基金supported by grants from the Macao Science and Technology Development Fund(No.0069/2021/AFJ)the Macao University of Science and Technology Faculty Research Grants(No.FRG-22-022-FMD).
文摘Recent advancements in the field of medical artificial intelligence(AI)have led to the widespread adoption of foundational and large language models.This review paper explores their applications within medical AI,introducing a novel classification framework that categorizes them as disease-specific,general-domain,and multi-modal models.The paper also addresses key challenges such as data acquisition and augmentation,including issues related to data volume,annotation,multi-modal fusion,and privacy concerns.Additionally,it discusses the evaluation,validation,limitations,and regulation of medical AI models,emphasizing their transformative potential in healthcare.The importance of continuous improvement,data security,standardized evaluations,and collaborative approaches is highlighted to ensure the responsible and effective integration of AI into clinical applications.
