1. Research on Multimodal Medical Image Feature Fusion Algorithm and Development of Intelligent Diagnosis Model

This research direction focuses on the development of multimodal medical image feature fusion algorithms and intelligent diagnosis models. Aiming at the clinical limitations of insufficient feature representation, limited lesion recognition ability and low quantitative diagnosis accuracy of single-modal medical images, this work conducts systematic research on standardized image preprocessing, multimodal registration and alignment, and deep feature fusion based on CT, MRI, ultrasound and other mainstream medical imaging data. This research constructs high-precision and interpretable models for lesion segmentation, disease classification and intelligent evaluation, effectively explores complementary information from multimodal images, improves the capabilities of intelligent disease diagnosis and quantitative analysis, and provides core algorithmic and technical support for clinical precise imaging diagnosis.

2. Disease Mechanism Analysis and Precision Diagnosis and Treatment Modeling Based on Multi-dimensional Omics Features

Based on multi-dimensional omics systems including genomics, transcriptomics and metabolomics, this direction focuses on the molecular mechanism analysis and precise diagnosis and treatment modeling of major diseases. It establishes standardized pipelines for multi-omics data cleaning, feature screening and enrichment analysis, and deeply excavates disease-specific molecular biomarkers and core pathogenic regulatory pathways. This research addresses the defects of one-sided mechanism interpretation and inaccurate clinical prediction caused by single-omics analysis, and constructs models for disease typing, prognostic evaluation and drug efficacy prediction, providing solid theoretical and technical support for disease mechanism research and individualized precision medicine.

3. Screening and Optimization of Novel Antigen Polypeptides and Research on Vaccine Immune Efficacy

This direction focuses on the screening, optimization and immune efficacy improvement of novel polypeptide vaccines. With bioinformatics-based epitope prediction, polypeptide sequence modification and structural optimization technologies, high-specificity and high-immunogenicity dominant antigen polypeptides are precisely screened. Combined with adjuvant compatibility and delivery system optimization strategies, a novel polypeptide vaccine system is constructed. This research systematically explores the mechanisms by which polypeptide vaccines activate specific humoral and cellular immune responses, comprehensively evaluates vaccine immune protection efficacy, and solves the shortcomings of traditional vaccines such as insufficient safety, poor targeting and long preparation cycles, providing innovative technical solutions for immune prevention and treatment of infectious diseases and tumors.

4. Multi-omics and Imaging Cross-modal Feature Fusion Research for Precision Diagnosis and Treatment

This direction focuses on cross-modal feature fusion of multi-omics molecular data and medical imaging data as well as its applications in precision diagnosis and treatment. Aiming at the key challenges of strong data heterogeneity, ambiguous correlation mechanisms and insufficient fusion modeling accuracy between molecular omics and imaging phenotypic data, adaptive cross-modal feature fusion algorithms are developed. This research deeply explores the coupling relationship between microscopic molecular features and macroscopic imaging phenotypes, constructs cross-scale and high-generalization models for precise disease typing, early warning and prognostic prediction, and realizes comprehensive and accurate judgment from molecular mechanisms to clinical phenotypes, promoting the technological upgrading of precision medicine.

5. Clinical-oriented Development and Application of Intelligent Biomedical Diagnosis and Treatment Systems

Focusing on practical clinical diagnosis and treatment demands and the implementation of smart healthcare, this direction conducts research on the development and application of intelligent biomedical diagnosis and treatment systems. It integrates multi-source biomedical information including multi-omics data, medical images, clinical medical records and follow-up data to complete system architecture design, functional module development and iterative optimization. The system integrates core functions such as intelligent diagnosis, risk assessment, auxiliary decision-making and health management. Taking system stability, compatibility and practicality into account, this research aims to build implementable and reusable intelligent diagnosis and treatment platforms, promote the clinical transformation of biomedical big data, and facilitate the large-scale application of smart medical services.