Graph Convolutional Neural Networks for Medical Image Analysis
Deep Learning-based Artificial Intelligence, in particular Convolutional Neural Networks (CNNs), has seen exponential growth in many pattern recognition challenges since the early 2010s. While CNNs were originally developed to act on data defined on regular cartesian grids, such as images, audio, and text data, they have recently been extended to work on irregularly connected data, such as graphs. This opens the door to a whole new set of potential applications where graphs arise, either naturally (e.g. surface or volumetric meshes representing shapes) or as a useful intermediate representation (e.g. superpixels representing a medical image). The project will apply graph CNNs to medical image analysis. For example, to identify and outline (segment) a specific structure of interest in a medical image. This is useful in many clinical areas, including cancer (e.g. tumour segmentation to determine treatment effect), neurodegenerative disorders (e.g. parcellation to detect early dementia), cardiovascular diseases (e.g. quantifying coronary artery disease), and musculoskeletal problems.
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