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Scheme : Linkage Projects
Research Topic : self image
Socio-Economic Objective : Diagnostic methods
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  • Researchers (44)
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  • Funded Activity

    Linkage Projects - Grant ID: LP100100272

    Funder
    Australian Research Council
    Funding Amount
    $119,000.00
    Summary
    Microchip Impedance Biosensor for Biomedical Diagnostics. This research proposal uses an innovative engineering approach based on novel nanomaterials with the aim of developing a new and generic biosensing technology with the potential to be widely applied in many areas including medical diagnostics, environmental control, industry and biosecurity. The outcomes from this project will benefit Australia by contributing through the development of novel materials, new technologies and new devices. .... Microchip Impedance Biosensor for Biomedical Diagnostics. This research proposal uses an innovative engineering approach based on novel nanomaterials with the aim of developing a new and generic biosensing technology with the potential to be widely applied in many areas including medical diagnostics, environmental control, industry and biosecurity. The outcomes from this project will benefit Australia by contributing through the development of novel materials, new technologies and new devices. The development of technological innovations based on fabricated nanomaterials, will also enhance capacity in frontier technology such as nanotechnology, and build Australia’s strength in using new biosensing technologies.
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    Funded Activity

    Linkage Projects - Grant ID: LP0212081

    Funder
    Australian Research Council
    Funding Amount
    $331,196.00
    Summary
    Learning Medical Image Knowledge. We aim to develop Machine Learning and Knowledge Acquisition techniques for automated recognition of features in medical images, and to provide decision support for diagnosis from medical images. The project is innovative in its use of layered learning, where the computer first learns to recognise low-level image features that are then used to learn more complex features. The project is also innovative in combining a variety of automatic learning methods, includ .... Learning Medical Image Knowledge. We aim to develop Machine Learning and Knowledge Acquisition techniques for automated recognition of features in medical images, and to provide decision support for diagnosis from medical images. The project is innovative in its use of layered learning, where the computer first learns to recognise low-level image features that are then used to learn more complex features. The project is also innovative in combining a variety of automatic learning methods, including relational learning, with human-assisted knowledge acquisition methods. The expected outcomes will be new techniques for image understanding, particularly for our test domain, namely, High Resolution Computed Tomography scans of the lung.
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    Funded Activity

    Linkage Projects - Grant ID: LP0669420

    Funder
    Australian Research Council
    Funding Amount
    $147,900.00
    Summary
    Quantitative measurement of Schizophrenia using Electrovestibulography. Schizophrenia was estimated to cost approximately $1.85billion in 2001 (0.3% of GDP and nearly $50k for each of the 37,000 Australians with the illness). Over one third of the cost is borne by sufferers and their carers. Misdiagnosis and incorrect therapy are common. To date quantitative assessment of Schizophrenics has been impossible making this tool potentially invaluable. An accurate diagnostic test could facilitate earl .... Quantitative measurement of Schizophrenia using Electrovestibulography. Schizophrenia was estimated to cost approximately $1.85billion in 2001 (0.3% of GDP and nearly $50k for each of the 37,000 Australians with the illness). Over one third of the cost is borne by sufferers and their carers. Misdiagnosis and incorrect therapy are common. To date quantitative assessment of Schizophrenics has been impossible making this tool potentially invaluable. An accurate diagnostic test could facilitate earlier diagnosis, more accurate treatment plans, and prevention of debilitating psychotic episodes for the sufferer. By being able to monitor drug efficacy the community can benefit by reduced drug costs, confinement times and hastened new drug development.
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    Funded Activity

    Linkage Projects - Grant ID: LP0775196

    Funder
    Australian Research Council
    Funding Amount
    $253,000.00
    Summary
    Automated pathogen detection using time-gated luminescence microscopy. A rapid and general means of in-situ pathogen identification would benefit the community by ensuring that appropriate treatments can be applied in the early stages of a disease. Patient prognosis is thereby improved and opportunities for multi-drug resistant organisms to arise are limited. Time-gated luminescence microscopy (TgM) exploits persistent luminescence to overcome autofluorescence, a serious problem in pathogen dete .... Automated pathogen detection using time-gated luminescence microscopy. A rapid and general means of in-situ pathogen identification would benefit the community by ensuring that appropriate treatments can be applied in the early stages of a disease. Patient prognosis is thereby improved and opportunities for multi-drug resistant organisms to arise are limited. Time-gated luminescence microscopy (TgM) exploits persistent luminescence to overcome autofluorescence, a serious problem in pathogen detection. Drug-resistant 'Golden Staph' (MRSA) will be used as the model organism to evaluate TgM efficacy. Ultimately however, TgM will be applied for the detection of tuberculosis, a highly contagious disease affecting the respiratory system of more than one-third of the world's population.
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    Funded Activity

    Linkage Projects - Grant ID: LP0991295

    Funder
    Australian Research Council
    Funding Amount
    $280,000.00
    Summary
    Feature Detection and Computer-aided Diagnosis with Longitudinal Tracking of Benign Asbestos-Related Pleural Disease in CT images. This project seeks to develop smart and novel feature extraction and knowledge acquisition techniques to assist radiologists in automated diagnosis and assessment of lung diseases. These outcomes will lead to improved delivery of health services in Australia, including in rural regions. They will lead to more accurate assessment of asbestos related pleural diseases f .... Feature Detection and Computer-aided Diagnosis with Longitudinal Tracking of Benign Asbestos-Related Pleural Disease in CT images. This project seeks to develop smart and novel feature extraction and knowledge acquisition techniques to assist radiologists in automated diagnosis and assessment of lung diseases. These outcomes will lead to improved delivery of health services in Australia, including in rural regions. They will lead to more accurate assessment of asbestos related pleural diseases for compensation and treatment and better followup, leading to earlier treatment and better quality of life for patients suffering from lung diseases. The project will also save costs due to automated assessment as well as the potential for fewer patient scans.
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