Computer Vision News 34 Runner Up Best Poster Award at MIDL Graph neural networks (GNNs) have emerged as a promising approach to enhance the accuracy and efficiency of predicting aneurysm development in the brain. This innovative method represents the brain’s vasculature as a graph, providing a unique understanding of the structure of vessels and potential risks for aneurysms. In recent years, deep neural networks (DNNs) have been shown to be prone to miscalibration, leading to some unreliable predictions. Overconfident probability estimates often cause this miscalibration. By contrast, GNNs tend to be underconfident in their predictions. Previous research has attempted to mitigate the issue of underconfidence in GNNs, but the effectiveness of these techniques remains largely untested in the context of medical image data. This paper aims to address that by determining whether calibration techniques applied to overconfidence in DNNs could be generalized to fix underconfidence in GNNs trained on medical image data. Iris Vos (left) is a fourth-year PhD student at UMC Utrecht in the Netherlands. Her work on the risk prediction of aneurysm development in the brain has just won the Runner Up Best Poster Award at MIDL2023. Calibration Techniques for Node Classification Using Graph Neural Networks on Medical Image Data
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