Large AI models for biomedicine
Project Description
This project aims to develop and deploy large-scale artificial intelligence models specifically trained on massive, multimodal biomedical data. These foundational models will serve as a new scientific instrument for decoding complex biology, accelerating drug discovery, and enabling personalized medicine. By integrating genomic, proteomic, clinical, and research data, we aim to develope large AI models to uncover hidden disease mechanisms, predict patient outcomes, and identify novel therapeutic targets with unprecedented speed and scale.
Supervisor
CHEN, Hao
Quota
10
Course type
UROP1000
UROP1100
UROP2100
UROP3100
UROP3200
UROP4100
Applicant's Roles
Data processing and paper writing.
Applicant's Learning Objectives
The primary objective of this research proposal is to equip students with the knowledge and skills necessary to conduct research on the application of vision language models in medical image analysis. Students will learn about the fundamental concepts of deep learning, computer vision, and natural language processing, as well as their applications in medical image analysis. They will also gain practical experience in developing and training vision language models for medical image analysis using state-of-the-art techniques and tools. Additionally, students will learn how to evaluate the performance of vision language models using relevant metrics and analyze the results. By the end of the project, students will have developed a deeper understanding of the potential impact of vision language models on medical image analysis and the broader field of healthcare.
Complexity of the project
Challenging