Computer Vision News 36 for instance between malignant and non-malignant tumors. In the context of aneurysm development, the objective is to determine subgroups within the population to identify individuals at higher risk. The confidence estimates produced by neural networks play a pivotal role in clinical decision-making. “If you use neural networks, and the produced confidence estimates are over or underconfident, it can lead to real issues in the clinic,” Iris points out. “If we want to identify if a certain person is at risk of developing an aneurysm, and we say it’s 70%, you need to know that your model is not over or underconfident. We want to use these models to decide whether or not we perform follow-up screenings of at-risk individuals!” Although other techniques have already proved to work well, she hopes people will incorporate the essence of this research in their work. It demonstrates how relatively simple it is to calibrate a model or at least report on its uncertainty rather than reporting solely on how a model has obtained a degree of accuracy. Evaluating the uncertainty of a model brings it closer to clinical acceptance. Last year, Iris worked on an interesting project using GNNs for automated intracranial artery labelling, using an atlas to extract atlas-based features that were used as input for node classification. It was named a finalist for an award at the SPIE conference. Runner Up Best Poster Award at MIDL Graph neural networks learn by exchanging information between local neighborhoods of nodes. By adding information on a global scale, using features based on a statistical brain atlas, we were able to improve the performance of node classification
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