Robust, valid and interpretable deep learning for quantitative imaging. One of the biggest challenges in employing artificial intelligence is the “black-box” nature of the models used. This project aims to improve the effectiveness and trustworthiness of deep learning within quantitative magnetic resonance imaging. Deep learning has great promise in speeding-up complex image processing tasks, but currently suffers from variable data inputs, predictions are not guaranteed to be plausible and it i ....Robust, valid and interpretable deep learning for quantitative imaging. One of the biggest challenges in employing artificial intelligence is the “black-box” nature of the models used. This project aims to improve the effectiveness and trustworthiness of deep learning within quantitative magnetic resonance imaging. Deep learning has great promise in speeding-up complex image processing tasks, but currently suffers from variable data inputs, predictions are not guaranteed to be plausible and it is not clear to the end user how reliable the results are. The outcomes intend to deliver advanced knowledge and capability in artificial intelligence and machine learning that Australia urgently needs to capitalise on bringing deep learning into practical applications delivering economic, commercial and social impact.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.
Personalised Learning for Per-pixel Prediction Tasks in Image Analysis. AI-assisted image segmentation & synthesis are very challenging and usually require pixel-level labelling (per-pixel prediction) that is costly to obtain. The small amount of labels makes it difficult to train an “optimal” unified model for varied data as conventional methods did. This project aims to develop a new paradigm “personalised learning” to tackle this problem, where each image could be dealt with a model tailored ....Personalised Learning for Per-pixel Prediction Tasks in Image Analysis. AI-assisted image segmentation & synthesis are very challenging and usually require pixel-level labelling (per-pixel prediction) that is costly to obtain. The small amount of labels makes it difficult to train an “optimal” unified model for varied data as conventional methods did. This project aims to develop a new paradigm “personalised learning” to tackle this problem, where each image could be dealt with a model tailored to individual characteristics. The success of this project could significantly advance the fundamental research in image analysis. Expected outcomes include new knowledge and algorithms for image analysis, which could benefit fields like biology and archaeology, where labeled images are hard to attain and scarce.Read moreRead less
The cellular basis of branching morphogenesis during kidney development. This project aims to study the process of branching morphogenesis which drives the development of the kidney. Previous studies group have demonstrated, in general terms, how branching progresses during gestation. However, little is known about the fundamental cellular events which trigger or characterise this basic developmental process. This project expects to provide deep insights into the cellular basis of tissue and org ....The cellular basis of branching morphogenesis during kidney development. This project aims to study the process of branching morphogenesis which drives the development of the kidney. Previous studies group have demonstrated, in general terms, how branching progresses during gestation. However, little is known about the fundamental cellular events which trigger or characterise this basic developmental process. This project expects to provide deep insights into the cellular basis of tissue and organ development. In studying this process the project should provide critical insights into how cells act, individually and collectively, to build tissues.Read moreRead less
Improved image analysis: maximised statistical use of geometry/shape constraints. This project will improve image analysis to apply such applications as 3D street-scape reconstruction, synthetic inserts into video for special effects, autonomous navigation, and scene understanding. It will do so by maximally exploiting the geometry of planar surfaces (e.g. walls) and straight lines and other simple geometric shapes.
The Circus Oz Living Archive: developing a model of online digital engagement for the performing arts. The performing arts play a crucial role in defining our national identity. Circus Oz, as a flagbearer of contemporary Australian cultural identity around the globe, shares the challenges faced by the sector, including increasing competition for audiences and changing expectations about the relationship between audiences and creative content. Cultural dialogue and creative activity increasingly ....The Circus Oz Living Archive: developing a model of online digital engagement for the performing arts. The performing arts play a crucial role in defining our national identity. Circus Oz, as a flagbearer of contemporary Australian cultural identity around the globe, shares the challenges faced by the sector, including increasing competition for audiences and changing expectations about the relationship between audiences and creative content. Cultural dialogue and creative activity increasingly occur online. The performing arts need to explore novel ways to deepen online community engagement to strengthen their artform. This project researches how digital technologies can help the performing arts employ their documented cultural heritage to drive innovations in repertoire development, performance scholarship and audience interaction.Read moreRead less