Discovery Early Career Researcher Award - Grant ID: DE160101518
Funder
Australian Research Council
Funding Amount
$294,111.00
Summary
Multi-Object Recognition of Biomedical Images via Holistic Ontology. This project seeks to advance the development of new biomedical image recognition and analysis solutions by associating biomedical images with biomedical knowledge and personalised data. The provision of accurate and robust multi-object recognition and analysis from biomedical image data is a fundamental requirement for biomedical imaging applications. This project aims to improve the recognition and analysis of anatomical and ....Multi-Object Recognition of Biomedical Images via Holistic Ontology. This project seeks to advance the development of new biomedical image recognition and analysis solutions by associating biomedical images with biomedical knowledge and personalised data. The provision of accurate and robust multi-object recognition and analysis from biomedical image data is a fundamental requirement for biomedical imaging applications. This project aims to improve the recognition and analysis of anatomical and functional structures from biomedical images with ‘holistic ontology’ modelling that represents a multi-level biological, physiological, and anatomical knowledge base. The project will potentially have application in many health care areas, such as computer aided diagnosis, image-guided surgery planning, and image-based disease modelling.Read moreRead less
A data science framework for modelling disease patterns from medical images. A data science framework for modelling disease patterns from medical images. This project aims to extract models of disease patterns from medical imaging data, using deep learning, smart image processing, machine learning, and statistical modelling to quantify and model patterns conventional methods cannot detect. These disease models are expected to improve understanding of particular diseases and enable precision medi ....A data science framework for modelling disease patterns from medical images. A data science framework for modelling disease patterns from medical images. This project aims to extract models of disease patterns from medical imaging data, using deep learning, smart image processing, machine learning, and statistical modelling to quantify and model patterns conventional methods cannot detect. These disease models are expected to improve understanding of particular diseases and enable precision medicine, which recognises that there are important differences between individuals with a particular disease, and that when patients are separated into sub-populations with similar disease patterns, treatment can be tailored to these sub-populations.Read moreRead less
Omni-modality medical image analysis and visualisation. The term ‘Omni’-modality imaging (OMI) has been coined to describe the integration of multiple, complementary medical imaging modalities. However, there is currently a lack of an appropriate means to assimilate and derive maximum benefit from these integrated data. This project aims to provide a new approach to OMI data analysis and visualisation, by deriving a novel ‘level of relevance’ from the overlapping anatomical and pathological stru ....Omni-modality medical image analysis and visualisation. The term ‘Omni’-modality imaging (OMI) has been coined to describe the integration of multiple, complementary medical imaging modalities. However, there is currently a lack of an appropriate means to assimilate and derive maximum benefit from these integrated data. This project aims to provide a new approach to OMI data analysis and visualisation, by deriving a novel ‘level of relevance’ from the overlapping anatomical and pathological structures in the data which will be used to suppress superfluous data and highlight the most relevant data to maximise the information gained from the OMI data. Further, OMI visualisation is proposed to efficiently navigate through the overlapping data.Read moreRead less