Research On Medical Image Analysis Technology Based On Multi-view Learning

Medical image analysis technology is playing an increasingly important role in the modern medical field.It helps doctors to diagnose and treat conditions more quickly and accurately,thus improving the quality and reducing the cost of medical care.To ad-dress the challenges in medical image analysis,we propose a holistic framework involv-ing cross-view unsupervised medical image domain adaptation segmentation,multi-view pathology image incremental learning,and multi-view pathology image diagnosis.These three works together address the problems of multi-viewness and inadequate data anno-tation in medical image analysis to provide more accurate,faster,and reliable diagnostic services for doctors and patients.First,we propose an unsupervised medical image domain adaptation segmentation algorithm enhanced by semantic similarity constraints to address the problem of lack of view data annotation in multi-view medical images.The algorithm designs a semantic similarity mining module and utilizes a new semantic similarity constraint strategy to ex-plicitly exploit the semantic similarity between the target and source domains to enhance the extraction of view invariant features and thus improve the segmentation accuracy.Second,medical analysis software is often put into practical use in situations where physicians need to make corrections to diagnostic results,and the annotation informa-tion obtained in this step is often not utilized.In this paper,we propose an incremental learning algorithm designed for multi-view pathology images.The algorithm can quickly update the existing model when new data arrives and avoid forgetting the old knowledge.This allows the algorithm to continuously update the model according to the physician’s corrections,thus improving the accuracy and robustness of the algorithm.Finally,we combine the above two contribution points to the application of pediatric tumor pathology image diagnosis,and designs and develops a multi-view pediatric tumor pathology image diagnosis software.The software implements three multi-view pathol-ogy image diagnosis algorithms,which can perform fast and accurate diagnosis of multi-ple tumor pathology images with a user-friendly interface.The software has a wide range of applications and can be used in hospitals,clinics,academic research institutions,and other settings.The goal is to provide physicians and patients with more accurate,faster,and more reliable diagnostic services to help improve the success and survival rates of pediatric oncology treatment.Through experimental results on three datasets,we have validated the effectiveness of the proposed method.