Application of Artificial Intelligence to Enhance the Fluorescence Microscopy of Circulation Tumor Cells Captured by MEF Chips
Project Description
Cancer is one of the top killers in Hong Kong and the other world. Conventional cancer diagnostics using invasive biopsy and/or positron emission tomography in hospitals is expensive and time-consuming. Circulation tumor cells (CTCs) in human blood has been shown to be one new liquid biopsy for cancer diagnostics. The Microfluidic Elasto-Filtration (MEF) Chips have been used for personalized detection of CTCs in the clinical study in the collaborative hospital. However, the fluorescence microscopy of CTCs captured by MEF chips is labor-intensive and time-consuming. This project is to apply open-source Transformer-based AI model for automatic detection of CTC
Supervisor
LEE Yi-Kuen
Quota
4
Course type
UROP1000
UROP1100
UROP2100
UROP3100
UROP3200
UROP4100
Applicant's Roles
The student(s) will develop a computer program to analyze the fluorescence micrographs obtained from the multi-color fluorescence microscopy of microfluidic elasto-filtration (MEF) CTC chips for cancer diagnosis. The MEF CTC chips will be fabricated at HKUST Nanosystem Fabrication Facility. The program will perform the following tasks: apply the open-source Transformer-based AI model to process the fluorescence micrographs of CTCs to achieve the automatic detection of CTC.
Applicant's Learning Objectives
Develop an understanding of cancer and CTCs
Develop an understanding of multi-color fluorescence microscopy for CTCs
Develop computational skills to design and analyze the digital fluorescence micrographs from MEF chips.
Develop the skill to apply open-source Transformer-based AI modelto process the fluorescence micrographs of CTCs
Develop and practice technical communication skill
Develop an understanding of multi-color fluorescence microscopy for CTCs
Develop computational skills to design and analyze the digital fluorescence micrographs from MEF chips.
Develop the skill to apply open-source Transformer-based AI modelto process the fluorescence micrographs of CTCs
Develop and practice technical communication skill
Complexity of the project
Moderate