Automated analysis of multi-modal medical data using deep belief networks. This project will develop an improved breast cancer computer-aided diagnosis (CAD) system that incorporates mammography, ultrasound and magnetic resonance imaging. This system will be based on recently developed deep learning techniques, which have the capacity to process multi-modal data in a unified and optimal manner. The advantage of this technique is that it is able to automatically learn both the relevant features t ....Automated analysis of multi-modal medical data using deep belief networks. This project will develop an improved breast cancer computer-aided diagnosis (CAD) system that incorporates mammography, ultrasound and magnetic resonance imaging. This system will be based on recently developed deep learning techniques, which have the capacity to process multi-modal data in a unified and optimal manner. The advantage of this technique is that it is able to automatically learn both the relevant features to analyse in each modality and the hidden relationships between them. The use of deep belief networks has produced promising results in several fields, such as speech recognition, and so this project believes that our approach has the potential to improve both the sensitivity and specificity of breast cancer detection.Read moreRead less
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
A theoretical framework for practical partial fingerprint identification. Fingerprints captured from a crime scene are often partial and poor quality which makes it difficult to identify the criminal suspects from large databases. This project will find mathematical models which can estimate the missing information located in the blank areas of a partial fingerprint and effectively identify it.
Dynamic Visual Scene Gist Recognition using a Probabilistic Inference Framework. How can we see the forest without intentionally looking for the trees? How can we tell traffic is flowing smoothly on a busy highway without identifying vehicles or measuring their speed? These are the questions that inspire this research project. Humans are endowed with the ability to grasp the ‘gist’ or overall meaning of a complex visual scene from a single glance and without attention to details. The aim of this ....Dynamic Visual Scene Gist Recognition using a Probabilistic Inference Framework. How can we see the forest without intentionally looking for the trees? How can we tell traffic is flowing smoothly on a busy highway without identifying vehicles or measuring their speed? These are the questions that inspire this research project. Humans are endowed with the ability to grasp the ‘gist’ or overall meaning of a complex visual scene from a single glance and without attention to details. The aim of this project is to develop new computational vision models that combine biological visual processing with probabilistic inference for gist recognition. The developed models will be able to mimic human vision by analysing a complex dynamic scene rapidly and classifying its semantic categories, without identifying individual objects.Read moreRead less