Yongcheng Yao
I am a doctoral researcher at the UKRI CDT in Biomedical AI, University of Edinburgh. Previously, I was a research assistant at the CU Lab of AI in Radiology (CLAIR), Chinese University of Hong Kong (CUHK), where I worked with Prof. Weitian Chen. I obtained my M.Phil. in Imaging and Interventional Radiology from CUHK in 2020, and my B.Eng. in Biomedical Engineering from South China University of Technology in 2017.
Research Interest
[Machine Learning, Computer Vision, Probabilistic ML, Data-efficient ML]
[medical image analysis, deep learning, segmentation, registration, classification, model bias & fairness, data imbalance, domain adaption] (updated on Jan 2024)
My research interests lie in the intersection of medical image analysis and deep learning/artificial intelligence (DL/AI). My previous research projects are about DL/AI for medical image segmentation, registration, and classification. In addition to exploring efficient model structures and training strategies, I believe the interpretability of deep learning models is an important research topic. As such, I am also interested in explainable ML, probabilistic ML, and model bias & fairness. Generally, I am open to biomedical computer vision & pattern recognition tasks.
In general, my passion lies not only in developing efficient algorithms to solve technical problems but also in bridging the gap between research and practical applications. I am interested in tackling various challenges in biomedical AI, including data privacy, data imbalance, and domain shift problems. In the coming years, I will focus on the intersection field of medical image analysis and deep learning, with a vision to improve clinical workflow via technical innovation.
Previously, I was working on computational neuroscience. Specifically, we analyzed structural and functional brain MR images using morphometrics, statistical parametric mapping, connectivity analysis, graph-theory-based analysis, and machine learning.
Latest News
[01/2024] A work on automatic and quantitative T1rho analysis from knee MRI was accepted for presentation @ISMRM2024.
[10/2023] A paper on unsupervised domain adaptation for osteoarthritis phenotype classification was online (co-first author).
[07/2023] Our paper “CartiMorph: a framework for automated knee articular cartilage morphometrics” was accepted for publication in Medical Image Analysis (MedIA).