Titlebook: Uncertainty for Safe Utilization of Machine Learning in Medical Imaging; 6th International Wo Carole H. Sudre,Raghav Mehta,William M. Wells

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Conformal Performance Range Prediction for Segmentation Output Quality Controle techniques hold potential for intuitive output quality control. However, such performance estimates rely on calibrated softmax outputs, which is often not the case in modern neural networks. Moreover, the estimates do not take into account inherent uncertainty in segmentation tasks. These limitati

在前面

Holistic Consistency for Subject-Level Segmentation Quality Assessment in Medical Image Segmentationegmentation map produced by a segmentation model, it is desired to have an automatic, accurate, and reliable method in the pipeline for segmentation quality assessment (SQA) when the ground truth is absent. In this paper, we present a novel holistic consistency based method for assessing at the subj

surrogate

CROCODILE: Causality Aids RObustness via COntrastive DIsentangled LEarningaper, we introduce our CROCODILE framework, showing how tools from causality can foster a model’s robustness to domain shift via feature disentanglement, contrastive learning losses, and the injection of prior knowledge. This way, the model relies less on spurious correlations, learns the mechanism

Digitalis

Image-Conditioned Diffusion Models for Medical Anomaly Detection and the original can localise arbitrary anomalies whilst also providing interpretability for an observer by displaying what the image ‘should’ look like. All existing reconstruction-based methods have a common shortcoming; they assume that models trained on purely normal data are incapable of repro

致敬

Information Bottleneck-Based Feature Weighting for Enhanced Medical Image Out-of-Distribution Detectithin medical image settings, OOD data can be subtle and non-obvious to the human observer. Thus, developing highly sensitive algorithms is critical to automatically detect medical image OOD data. Previous works have demonstrated the utility of using the distance between embedded train and test feat

ENNUI

Beyond Heatmaps: A Comparative Analysis of Metrics for Anomaly Localization in Medical Imageson this concept, un- or weakly supervised anomaly localization approaches have gained popularity. These methods aim to model normal or healthy samples using data and then detect deviations (i.e., abnormalities). However, as this is an emerging field situated between image segmentation and out-of-dis

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