AUSLearn: AUtomated Sample Learning for Object Recognition. This project aims to enable computers to learn how to effectively use training samples for object recognition. Training sample is the only source used by computers to learn recognising objects. This project creates a new research direction that will enable the first full exploration of the power of samples. The aims will be enabled by leveraging the recent advances in reinforcement learning, fast training algorithms, and by developing n ....AUSLearn: AUtomated Sample Learning for Object Recognition. This project aims to enable computers to learn how to effectively use training samples for object recognition. Training sample is the only source used by computers to learn recognising objects. This project creates a new research direction that will enable the first full exploration of the power of samples. The aims will be enabled by leveraging the recent advances in reinforcement learning, fast training algorithms, and by developing novel deep learning algorithms. The new algorithms will benefit a wide range of applications, e.g. to effectively use car crash training samples for accurately identifying potential road crashes in transport and to effectively use rare medical imaging training data for robustly diagnosing diseases in health.Read moreRead less
Efficient learning from multiple brain imaging data sets. Brain imaging data analysis methods have proven to be very effective in the study of brain functions and the identification of brain disorders because they minimise the modelling assumptions on the underlying structure of the problem. Analysis of multiple brain imaging data sets, either of the same modality as in multitask or multisubject data sets or from different modalities as in the case of data fusion, is a challenging problem in bi ....Efficient learning from multiple brain imaging data sets. Brain imaging data analysis methods have proven to be very effective in the study of brain functions and the identification of brain disorders because they minimise the modelling assumptions on the underlying structure of the problem. Analysis of multiple brain imaging data sets, either of the same modality as in multitask or multisubject data sets or from different modalities as in the case of data fusion, is a challenging problem in biomedical image analysis. This project will lead to fundamental contributions as well as techniques that address both problems: extraction of relevant features information from multisubject brain imaging data sets of the same modality or from fusion of brain imaging data sets collected from multimodalities.Read moreRead less
A novel approach to diffusion MRI for greatly improved imaging of brain white matter and its connectivity. In this project, innovative new imaging and reconstruction techniques will be developed to provide images of brain connectivity, with unprecedented detail. Such images will allow extremely detailed investigations into the white matter connections that allow brain regions to communicate, and improve our understanding of how the brain operates.
Discovery Early Career Researcher Award - Grant ID: DE160100241
Funder
Australian Research Council
Funding Amount
$300,000.00
Summary
Learning Network Structures from Neuroimages for Diagnosing Brain Diseases. This project aims to develop a probabilistic inference framework based on graphical models to enable discriminative, interpretable and reliable analysis of brain imaging data. Recent development of computer-assisted neuroimage analysis calls for advanced pattern recognition methods. To meet this requirement, this project proposes a framework that addresses several critical issues in this process, and to provide important ....Learning Network Structures from Neuroimages for Diagnosing Brain Diseases. This project aims to develop a probabilistic inference framework based on graphical models to enable discriminative, interpretable and reliable analysis of brain imaging data. Recent development of computer-assisted neuroimage analysis calls for advanced pattern recognition methods. To meet this requirement, this project proposes a framework that addresses several critical issues in this process, and to provide important models and algorithms to identify brain connectivity patterns and benefit the diagnosis of diseases. The output of this project is expected to include a set of effective computational algorithms and computer-assisted tools, which can help medical researchers to identify brain disorders with better precision, repeatability and objectivity.Read moreRead less