Self-Supervised Sequential Biomedical Image-Omics

自监督序贯生物医学图像组学

• 批准号: DE240100168
• 负责人: Dr Lei Bi
• 金额: $ 28.66万
• 依托单位: The University of Sydney
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Early Career Researcher Award
• 财政年份: 2024
• 资助国家: 澳大利亚
• 起止时间: 2024-01-01 至 2026-12-31
• 项目状态: 未结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DE240100168
• 关键词: Self; Supervised; Sequential; Biomedical; Image

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.

该项目旨在开发一种自我监督的连续生物医学图像组学模型，以揭示潜在的生物过程，例如正常或异常。连续生物医学图像是最先进的成像方式，可以描绘人体进展的变化。提出了新的自监督机器学习算法来从异构和未标记的序列图像中获取特征。然后，这些衍生特征将用于表征形态和功能变化，这为增进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，并在未来的医疗保健领域带来潜在的好处。