Self-Supervised Sequential Biomedical Image-Omics

该项目旨在开发一个自我监督的顺序生物医学图像组学模型，以揭示潜在的生物过程，例如，正常或不正常。连续生物医学图像是最先进的成像模式，其允许描绘进展到人体的变化。提出了新的自监督机器学习算法，以从异质和未标记的序列图像中提取特征。然后，这些衍生的特征将用于描述形态和功能变化，这为增加对个体受试者疾病进展的理解提供了机会。该项目的成果将为系统生物学提供新的见解，并在医疗保健领域具有潜在的未来利益。

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.