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; 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.

该项目旨在开发一个自我监督的顺序生物医学图像摩西模型，以揭示基本的生物学过程，例如正常或异常。顺序生物医学图像是最新的成像方式，可以描绘出对人体的进展变化。提出了新的自我监督机器学习算法，以从异源和未标记的顺序图像中得出特征。然后，这些派生的特征将用于表征形态学和功能变化，这为增加对个人受试者疾病进展的理解提供了机会。该项目的结果将为系统生物学提供新的见解，并在医疗保健中潜在的未来收益。