Damage Detection and Quantification using Infrastructure Digital Twins. Structural health monitoring is vital for infrastructure assets management as early detection of structural conditions is key to both safety and ongoing maintenance. This project combines computer vision, vibration tests, finite element modelling and deep learning technologies to develop an efficient structural health monitoring system. Digital twins created from images taken by cameras or UAVs will be correlated through dee ....Damage Detection and Quantification using Infrastructure Digital Twins. Structural health monitoring is vital for infrastructure assets management as early detection of structural conditions is key to both safety and ongoing maintenance. This project combines computer vision, vibration tests, finite element modelling and deep learning technologies to develop an efficient structural health monitoring system. Digital twins created from images taken by cameras or UAVs will be correlated through deep learning with structural conditions and load-carrying capacities obtained from vibration tests and finite element model analysis for efficient structural damage detection and quantification. The project will lead to effective structural health monitoring and enhance structural safety and reduce maintenance costs. Read moreRead less
Subband centroids and deep neural networks for robust speech recognition. This project aims to improve the robustness and accuracy of automatic speech and speaker recognition systems. Though these systems work reasonably well in noise-free environments, their performance deteriorates drastically even in the presence of a small amount of noise. To overcome this problem, this project proposes a missing-feature approach for robust speech and speaker recognition. This approach is expected to make th ....Subband centroids and deep neural networks for robust speech recognition. This project aims to improve the robustness and accuracy of automatic speech and speaker recognition systems. Though these systems work reasonably well in noise-free environments, their performance deteriorates drastically even in the presence of a small amount of noise. To overcome this problem, this project proposes a missing-feature approach for robust speech and speaker recognition. This approach is expected to make the speech and speaker recognition systems less sensitive to additive background noise and make them more useful in telecommunications and business.Read moreRead less
Adapting Deep Learning for Real-world Medical Image Datasets. The project aims to investigate new deep learning modelling approaches to leverage real-world large-scale image data sets that contain noisy and incomplete labels and imbalanced class prevalence – to enable the use of these data sets for modelling deep learning classifiers. Expected outcomes include an innovative method for modelling deep learning classifiers. The research will involve new inter-disciplinary and international collabor ....Adapting Deep Learning for Real-world Medical Image Datasets. The project aims to investigate new deep learning modelling approaches to leverage real-world large-scale image data sets that contain noisy and incomplete labels and imbalanced class prevalence – to enable the use of these data sets for modelling deep learning classifiers. Expected outcomes include an innovative method for modelling deep learning classifiers. The research will involve new inter-disciplinary and international collaborations with machine learning and medical image analysis research institutions. This should provide significant benefits, such as better understanding of deep learning theory, new deep learning applications that can use previously unexplored data sets, and training for the future Australian workforce.Read moreRead less
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
Human Cues for Robot Navigation. The world has many navigational cues for the benefit of humans: sign posts, maps and the wealth of information on the internet. Yet, to date, robotic navigation has made little use of this abundant symbolic information as a resource. This project will develop a robot navigation system that can navigate using information beyond the robot's range sensors by incorporating knowledge gained by reading room labels, following human route directions or interpreting maps ....Human Cues for Robot Navigation. The world has many navigational cues for the benefit of humans: sign posts, maps and the wealth of information on the internet. Yet, to date, robotic navigation has made little use of this abundant symbolic information as a resource. This project will develop a robot navigation system that can navigate using information beyond the robot's range sensors by incorporating knowledge gained by reading room labels, following human route directions or interpreting maps found on the web. This project will demonstrate the robot's navigation ability by comparing its performance with a human as it learns to find its way around campus by asking for directions, reading signs and maps, and searching the internet for clues.Read moreRead less
Deep reinforcement learning for discovering and visualising biomarkers. This project aims to develop novel methods for discovering and visualising optimal bio-markers from chest computed tomography images based on extensions of recently developed deep reinforcement learning techniques. The extensions proposed in this project will advance medical image analysis by allowing an efficient analysis of large dimensionality inputs in their original high resolution. In addition, this project will be the ....Deep reinforcement learning for discovering and visualising biomarkers. This project aims to develop novel methods for discovering and visualising optimal bio-markers from chest computed tomography images based on extensions of recently developed deep reinforcement learning techniques. The extensions proposed in this project will advance medical image analysis by allowing an efficient analysis of large dimensionality inputs in their original high resolution. In addition, this project will be the first approach capable of discovering previously unknown biomarkers associated with important clinical outcomes. The project will validate the approach on a real-world case study data set concerning the prediction of five-year survival of chronic disease.Read moreRead less
Bridging the gap between global mechanics and regional imaging in the lungs. The detailed mechanics of breathing are not well understood, due to a lack of regional lung measurement techniques. This project aims to develop a powerful analysis tool to image in vivo mechanical properties of the lungs. The expected outcome of this project is a novel platform for investigation and understanding of lung function, enabling information previously only available for the whole lung to be calculated for lo ....Bridging the gap between global mechanics and regional imaging in the lungs. The detailed mechanics of breathing are not well understood, due to a lack of regional lung measurement techniques. This project aims to develop a powerful analysis tool to image in vivo mechanical properties of the lungs. The expected outcome of this project is a novel platform for investigation and understanding of lung function, enabling information previously only available for the whole lung to be calculated for local lung regions within the body. The image analysis methods developed are intended to enable respiratory researchers to investigate lung function in unprecedented detail, leading to new insights into the workings of this complicated and vital organ. Read moreRead less
Hybrid imaging/modelling: A new paradigm for understanding the lung. Our lungs are essential to sustain our lives, yet the details of lung biomechanics are barely understood because the available tools, imaging, modelling and simulation have significant limitations. Imaging is largely limited to providing structural information; simulation is severely restricted by a lack of validation; and inverse modelling is critically hampered by a lack of spatially resolved inputs. The project’s multidiscip ....Hybrid imaging/modelling: A new paradigm for understanding the lung. Our lungs are essential to sustain our lives, yet the details of lung biomechanics are barely understood because the available tools, imaging, modelling and simulation have significant limitations. Imaging is largely limited to providing structural information; simulation is severely restricted by a lack of validation; and inverse modelling is critically hampered by a lack of spatially resolved inputs. The project’s multidisciplinary team is uniquely positioned to explore these problems through the hybridisation of world-leading functional lung imaging technology with state-of-the-art modelling. This project aims to provide, perhaps for the first time, the capacity to see details with the resolution of imaging, richness of modelling and reliability of the finest measurements.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE180101133
Funder
Australian Research Council
Funding Amount
$358,551.00
Summary
Linking structure and function: a new approach for understanding the lung. This project aims to develop a powerful analysis tool to measure gas transport and mixing within lungs. This project will study the mechanical workings of the lungs, using an innovative approach for analysis of lung images. The expected outcome of this project is a novel platform for investigation and understanding of lung function. It is anticipated that application of the project outcomes to medical challenges in the lo ....Linking structure and function: a new approach for understanding the lung. This project aims to develop a powerful analysis tool to measure gas transport and mixing within lungs. This project will study the mechanical workings of the lungs, using an innovative approach for analysis of lung images. The expected outcome of this project is a novel platform for investigation and understanding of lung function. It is anticipated that application of the project outcomes to medical challenges in the long-term will lead to improved diagnostics and treatments for lung diseases.Read moreRead less
Multi-modal virtual microscopy for quantitative diagnostic pathology. This project will contribute to the next generation of virtual microscopy systems that provide innovative features capable of significantly increasing the adoption of digital imaging technology throughout the field of diagnostic pathology. These tools will especially contribute to the screening and diagnosis of cervical, lung and bladder cancer.