Research

The AIM Lab works at the intersection of artificial intelligence and medical health, exploring applications of machine learning, deep learning, and multimodal data analysis in medical image analysis, clinical data modeling, disease risk prediction, and computer-aided diagnosis. The group aims to provide methodological support for smart healthcare, precision health management, and intelligent medical decision-making.

Current Research

Medical image analysis and intelligent modeling. The AIM Lab works with CT, MRI, and other medical imaging data to study image preprocessing, lesion segmentation, radiomics feature extraction, deep learning modeling, and interpretability analysis, exploring the value of AI in assisted medical image analysis and disease risk assessment.

The group focuses on the integrated use of medical imaging, clinical indicators, and multi-source health data. Through feature selection, multimodal feature fusion, machine learning prediction models, and intelligent computer-aided diagnosis methods, we aim to improve the accuracy and stability of disease prediction, prognosis assessment, and clinical decision support.

• Intelligent medical image analysis and feature extraction
• Clinical data modeling and disease risk prediction
• Multimodal feature fusion and intelligent computer-aided diagnosis

The group conducts research around medical imaging, clinical indicators, and multi-source health data, exploring applications of AI technologies in disease identification, risk assessment, prognosis prediction, and clinical decision support. Related work can provide technical support for medical data analysis, smart healthcare development, and precision health management.

Methodologically, the group primarily uses machine learning, deep learning, radiomics, multimodal fusion, and statistical modeling. These methods are combined with medical image processing, clinical variable selection, model training and validation, result interpretation, and visualization to build intelligent analysis models for real medical problems. The group emphasizes the connection between algorithms and clinical scenarios, focusing on model accuracy, robustness, and interpretability.

Biomedical AI Applications

The AIM Lab addresses real-world problems in medical and health contexts, exploring applications of artificial intelligence in medical image analysis, clinical data modeling, disease risk prediction, and intelligent computer-aided diagnosis. Through machine learning, deep learning, and multimodal data fusion, the group works to improve the efficiency of medical data analysis and provide technical support for clinical decision-making and precision health management.

• Medical image analysis
• Clinical data modeling
• Disease risk prediction
• Multimodal data fusion
• Intelligent computer-aided diagnosis
• Prognosis assessment
• Health management decision support

The group focuses on deploying AI methods in real medical scenarios, especially modeling around medical imaging, clinical indicators, and multi-source health data. By building interpretable, stable, and application-oriented intelligent models, we support disease identification, risk stratification, prognosis prediction, and clinical decision support.