Discovery Early Career Researcher Award - Grant ID: DE240100168
Funder
Australian Research Council
Funding Amount
$413,847.00
Summary
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 w ....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.Read moreRead less
Ultra-sensitive 3D molecular assays using total body PET and deep learning. Recent advances in biomedical engineering have led to the development of Total Body Positron Emission Tomography (TB-PET), the most sensitive imaging device to date. Despite these impressive engineering advances, computational methods lag far behind and model-based approaches cannot deal with the complexity or volume of data these systems produce. We will develop new computational methods based on deep learning and stati ....Ultra-sensitive 3D molecular assays using total body PET and deep learning. Recent advances in biomedical engineering have led to the development of Total Body Positron Emission Tomography (TB-PET), the most sensitive imaging device to date. Despite these impressive engineering advances, computational methods lag far behind and model-based approaches cannot deal with the complexity or volume of data these systems produce. We will develop new computational methods based on deep learning and statistical methods that fully exploit the richness and complexity of the data generated by TB-PET, enabling 3D quantitative assays of molecular processes throughout the entire human body with unparalleled sensitivity. The technology we create will open up new capability for the study of complex physiological systems.Read moreRead less
Tissue Bio-physicochemical Quantification Using Magnetic Resonance Imaging. This project aims to develop novel magnetic resonance imaging methods to investigate tissue structure and function. Current MRI technologies use standard water-based contrast mechanisms to generate images with limited tissue information. In contrast, this project expects to provide a non-invasive, ultra-high-resolution MRI technology that measures the electrical, magnetic, and chemical signals generated from the human bo ....Tissue Bio-physicochemical Quantification Using Magnetic Resonance Imaging. This project aims to develop novel magnetic resonance imaging methods to investigate tissue structure and function. Current MRI technologies use standard water-based contrast mechanisms to generate images with limited tissue information. In contrast, this project expects to provide a non-invasive, ultra-high-resolution MRI technology that measures the electrical, magnetic, and chemical signals generated from the human body. Thus, the new imaging methods can probe deeper biological functionality while examining tissue structure. The potential benefits include: expanding the scope and capabilities of current MRI, facilitating a wide range of imaging-based research and applications, and accelerating knowledge expansion in life science.Read moreRead less
Decoding the brain network of memory formation. This project aims to uncover how the brain network supports the formation of long-lasting memory using cutting-edge imaging, intervention and computational modelling. The project is anticipated to generate new knowledge of the neural activity and circuitry that facilitate memory formation, and targets for modulating network activity and behaviour. This will have significant benefits for neuroscience, engineering and imaging, as well as future appli ....Decoding the brain network of memory formation. This project aims to uncover how the brain network supports the formation of long-lasting memory using cutting-edge imaging, intervention and computational modelling. The project is anticipated to generate new knowledge of the neural activity and circuitry that facilitate memory formation, and targets for modulating network activity and behaviour. This will have significant benefits for neuroscience, engineering and imaging, as well as future applications in humans with technology for detecting, predicting and modulating cognitive performance.Read moreRead less