Design And Implementation Of Multimodal Medical Data Management System

In recent years,the medical level of our country develops rapidly,each hospital will produce a large number of medical data every year.Making full use of and mining these medical data information is of great significance for medical research and clinical diagnosis.How to make good use of these medical data for intelligent analysis to assist doctors in clinical diagnosis is the research hotspot in the field of medical image analysis.At present,deep learning method is more and more mature in the field of image processing,especially in medical image analysis in recent years.Data driven is one of the important characteristics of deep learning method.More precise annotation data can improve the accuracy of the model to a great extent.The thesis first analyzes the research background and application significance of the subject,clarifies the research objectives and content,and builds a system that can effectively manage medical data and provide users with auxiliary analysis functions.The thesis introduces related technologies from software development,medical image standards,deep learning,etc.,and conducts a detailed demand analysis of the project to establish the functions of the system.On the basis of demand analysis,this article describes the overall design and process of the system,divides the three main modules of medical data management,user management,and auxiliary analysis,and introduces the detailed design and implementation of each module.Finally,integrate the various modules,complete the system test and carry out the work summary and outlook.The multi-modal medical data management system implemented in this thesis can effectively manage multi-modal medical data such as MRI medical images,diagnosis reports,and annotation data.This system supports the efficient upload and download of various types of data,processes DICOM standard medical images,and realizes desensitization of sensitive information,providing data support for deep learning model training.In addition,the system also has the function of auxiliary analysis,invoking deep learning models through RPC,and online analysis of medical images.