Research Field:
Multimodal Medical Image Processing
Project Introduction：
Neurological diseases, including Stroke, Intracranial Aneurysm, Cerebral Small Vessel Disease (CSVD), and Amyotrophic Lateral Sclerosis (ALS), have become major public health challenges in the context of global population aging. These diseases are typically characterized by long disease courses, high heterogeneity, subtle early-stage symptoms, and significant inter-individual differences in disease progression. Traditional assessment approaches based on single imaging modalities or isolated clinical indicators often fail to achieve early screening, precise disease stratification, individualized progression prediction, and risk assessment for clinical intervention.
In recent years, the rapid accumulation of medical imaging data, electronic health records, genomics, blood-based biomarkers, and longitudinal follow-up data has created new opportunities for research on chronic neurological diseases. At the same time, the rapid advancement of artificial intelligence (AI), multimodal learning, and medical foundation models has made it possible to deeply mine disease-related patterns from complex, high-dimensional, and heterogeneous data sources, thereby enabling the development of precision biomarker systems.
This project aims to establish an intelligent analysis and precision biomarker research platform for neurodegenerative and cerebrovascular diseases based on multimodal medical data. The project will integrate multimodal information sources, including MRI, CT, clinical data, physiological indicators, and potential molecular biomarkers, to develop novel AI-driven approaches for early disease detection, risk prediction, disease progression assessment, and prognosis analysis. Research topics include brain structural and functional abnormality detection, intelligent cerebrovascular lesion analysis, disease progression modeling, personalized risk stratification, AI-assisted clinical intervention, and explainable medical AI.
In terms of translational applications, the project will further explore the integration and deployment of these AI capabilities and precision biomarker systems within the Ant Afu digital health platform. These technologies are expected to provide foundational intelligence and data support for functionalities such as intelligent medical report interpretation, health risk assessment, disease progression monitoring, longitudinal health record management, home-based follow-up, and personalized health recommendations for patients with chronic neurological diseases. The project aims to extend chronic neurological disease management from traditional hospital-centered care toward an integrated continuous healthcare model spanning both clinical and out-of-hospital settings.
The project features strong interdisciplinary integration and clinical translation potential, combining medical image analysis, machine learning, computer vision, neuroscience, and precision medicine. It seeks to transform chronic neurological disease management from traditional experience-based practice to a more precise, intelligent, and personalized paradigm, providing next-generation technological support for early screening, early diagnosis, risk warning, and precision intervention, while promoting the real-world deployment and translation of AI technologies in neurological disease diagnosis and digital health management.
Research Areas / Requirements:
-Currently pursuing a Bachelor's, Master's, or Ph.D. degree in Computer Science or other related STEM fields
-Background in Computer Science, Medical Imaging, Artificial Intelligence, or related disciplines, with experience in brain medical image analysis
-Familiar with deep learning, computer vision, or medical image analysis techniques (e.g., multimodal learning and generative models)
-Proficient in Python / C++ and mainstream frameworks such as PyTorch
-Strong research capabilities, engineering implementation skills, and teamwork spirit
-Prior experience in the above research areas, including industry experience or participation in laboratory research projects
Preferred Qualifications:
-Passionate about technical research, with the ability to generate novel ideas and innovative solutions; excellent self-learning, problem analysis, and problem-solving skills
-Strong curiosity and interest in medical data and neuroscience-related problems
-One or more publications in top-tier international conferences or core journals
-Availability for at least 3 months of full-time internship work
