Computer Vision News - October 2021

38 Poster Presentation Best of MICCAI 2021 Model calibration is especially important in medical applications, because the output score of the model should reflect its own trustworthiness . In other words, the model should be calibrated. A model is said to be calibrated if the score for an input corresponds to the probability of the prediction being correct. “ For example, from all the samples that are predicted with a probably of 0.9, we want 90 per cent to be accurately predicted, ” Teodora explains. “ We want this to hold for all probability scores. The problem is that modern neural networks are poorly calibrated, as shown in the 2017 paper ‘On Calibration of Modern Neural Networks’. They tend to give over-confident predictions. That’s why there is a growing interest in this field of model calibration. For model calibration we care about a quantity called calibration error. ” On the relationship between calibrated predictors and unbiased volume estimation Teodora Popordanoska is a PhD student at KU Leuven in Belgium, under the supervision of Matthew Blaschko. MICCAI this year is an extra special occasion for Teodora as she has had her first accepted conference paper! Her work investigates the relationship between model calibration and unbiased volume estimation. She spoke to us ahead of her poster session.