Medical Image Report Generation Method Based On Multi-network Fusion

Nowadays,different science and technology are developing rapidly,and medical imaging technology has also made rapid progress in the current era.Medical imaging plays an important role in clinical diagnosis and treatment,teaching and research science,etc.Analyzing and interpreting medical images while writing corresponding reports is an indispensable step in the current diagnosis and treatment process.The analysis and inter-pretation of medical images is a challenging task,and doctors may make mistakes due to fatigue or lack of sufficient experience,resulting in misdiagnosis of diseases and missing the best treatment time for patients;for experienced doctors,it is a time-consuming and tedious task,and causes a waste of medical resources.In this situation,computer-aided medical image report generation technology has become the need of the hour.Research on the application of artificial intelligence techniques for medical image report generation is still in its infancy and is currently limited to radiology image report generation,with no research on other medical image report generation techniques yet to emerge.In this thesis,an automatic medical image report generation method is proposed,and the main work includes the following points.(1)To address the problem of high similarity of massive medical image data,a med-ical image analysis method based on multi-network fusion is proposed,which uses multi-depth neural network fusion to extract pathological information from medical images and improves the ability of the model to extract various types of feature information from images in order to achieve accurate analysis of medical images.(2)To address the problem of different text characteristics of different components in image reports,different network models are used for report generation,in which dis-ease diagnosis is realized by automatic disease detection models,while description report generation is obtained by text generation models to ensure the accuracy of different parts of reports.(3)To make full use of the interconnection between different medical information,based on which a two-branch network structure is proposed and different network models for high similarity disease differentiation and automatic disease detection are refined for specific applications,and the causal linkage of medical information is applied to the model training to improve its prediction capability.(4)The text generation of the description report is realized based on the encoding-decoding architecture,in which the text decoder is a long and short-term memory network,the image encoder with the best effect is selected through comparison experiments,and the attention mechanism is added to focus on the regional information of the image while generating text to improve the accuracy of text generation.(5)Combining automatic disease detection and description report generation mod-els,an end-to-end medical image report generation framework is constructed to realize intelligent and fast medical image report generation.