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Project description: On behalf of the Victorian Orthopaedic Trauma Outcomes Registry (VOTOR), we will establish the role of artificial intelligence (AI) deep learning to improve the prediction
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segmentation and classification; for example, segmenting tumour from the medical images, and then classify the grade of the tumour. We will use various Deep Learning techniques, such as CNN, and will experiment
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Discovery Project, this research aims to develop highly novel physics-informed deep learning methods for Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) and applications in image
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for intelligent systems to guard their execution and evolution. Required knowledge - deep learning - software development lifecycle
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In recent years, AI techniques such as GANs and associated deep learning neural networks have become popular tools applied to the production and creation of works of art. In 2018, AI Art made
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techniques. It may also include hardware development of wearable assistive devices that use audio and haptic feedback. Required knowledge Image processing Computer vision Deep learning Programming (Python, C
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for monitoring and controlling the brain with medical devices and imaging brain activity in new and important ways. Required knowledge Statistical signal processing, Statistical Inference, Machine learning, Deep
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The existing deep learning based time series classification (TSC) algorithms have some success in multivariate time series, their accuracy is not high when we apply them on brain EEG time series (65
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the simple equation that more training data = better performance. Learning—in particular, the advanced deep learning methods, like BERT for NLP and ResNet for image processing—often require thousands
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unreliable techniques and is thus often not done so that infected colonies are discovered far too late. In this project, we aim to build Ai tools based on Deep Learning to automatically classify the health