Design And Implementation Of Artificial Intelligence Model Training Platform For Medical Field

The artificial intelligence model training platform for the medical field is a one-stop algorithm model development tool provided for algorithm engineers and medical workers to conduct research on artificial intelligence technology related to health care.It simplifies the management of data,models,and services for users and improves the efficiency of scientific research.At present,the functions of related products of artificial intelligence model training platforms at home and abroad have been quite complete,but the following problems and challenges still exist in the use of the medical field:(1)Difficulties in medical data management:Medical data is characterized by professionalism,complexity and diversity,with a wide variety of sources and formats.This poses challenges to the management,labelling and efficient use of medical data.(2)Inconsistent data standards for medical models when they are used on the ground:The standards used by devices in image generation are inconsistent,resulting in weak generalization capabilities of algorithm models when deployed and used.(3)Lack of professional support for the integration of medicine and industry:There is a lack of introduction and examples of application scenarios of artificial intelligence in the medical field,and it is difficult to guide the scientific research work that combines artificial intelligence and medical field.Based on the above difficulties and challenges,the main research contents of this paper are as follows:(1)A data management method and tool for medical scenarios is proposed and implemented.The tool automatically recognizes the file format of the original data uploaded by the user,calls the relevant converter interface according to different formats,and finally stores it in the platform standard format,which improves the labeling efficiency,data utilization and model development efficiency.(2)A medical image preprocessing scheme based on Fourier domain adaptation is proposed and implemented.The Fourier domain adaptive algorithm is used to process the data,eliminate the domain shift of the data,solve the problem of inconsistent data standards when the medical model is deployed from the data level,and improve the landing efficiency of the medical model.(3)Design and implement a medical artificial intelligence model training platform.The platform provides related papers,codes and datasets of artificial intelligence in the medical field,and also provides one-stop model development functions such as medical data annotation,medical model construction,medical model training,and medical model deployment.Finally,the platform was applied to the cooperative scientific research project with Hebei Eye Hospital,and the integrity,effectiveness and practicability of the platform were verified in the actual medicalengineering scientific research scenario.