The Future of Medical Imaging: Advancements in Analysis through Vision Language and Large Models
Project Description
Medical image analysis plays a crucial role in healthcare by providing a non-invasive way to diagnose and treat various medical conditions. With the advent of deep learning techniques, computer vision-based medical image analysis has achieved remarkable success in recent years. However, the current state-of-the-art methods are limited by the availability of large annotated datasets and the need for expert domain knowledge. This research proposal aims to explore the application of vision language models and large language models (e.g., CLIP, ChatGPT) in medical image analysis to address these limitations and improve the accuracy and efficiency of diagnosis.
Supervisor
CHEN, Hao
Quota
5
Course type
UROP1000
UROP1100
UROP2100
UROP3100
UROP3200
UROP4100
Applicant's Roles
The proposed research can have numerous applications in the medical field. It can aid in the automated detection of various diseases such as cancer, heart disease, and neurological disorders by analyzing medical images such as X-rays, MRIs, and CT scans. It can also assist doctors and radiologists in making more accurate and timely diagnoses, leading to better patient outcomes. The research can also be extended to drug discovery and personalized medicine by analyzing molecular images.
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