Self-Supervised Sequential Biomedical Image-Omics. This project aims to develop a self-supervised sequential biomedical image-omics model to uncover the underlying biological processes e.g., normal or

Description

Self-Supervised Sequential Biomedical Image-Omics. This project aims to develop a self-supervised sequential biomedical image-omics model to uncover the underlying biological processes e.g., normal or abnormal. Sequential biomedical images are state-of-the-art imaging modalities which allow to depict changes in progression to the human body. New self-supervised machine learning algorithms are proposed to derive features from heterogenous and unlabelled sequential images. These derived features will then be used to characterise the morphological and functional changes, which provide opportunities to increase understanding of progression of diseases of individual subject. The outcome from this project will provide new insights into system biology with potential future benefits in healthcare.. Scheme: Discovery Early Career Researcher Award. Field: 4603 - Computer Vision and Multimedia Computation. Lead: Dr Lei Bi