Titlebook: Uncertainty for Safe Utilization of Machine Learning in Medical Imaging; 4th International Wo Carole H. Sudre,Christian F. Baumgartner,Will

WAIL

Quantification of Predictive Uncertainty via Inference-Time Samplingeterministic network without changes to the architecture nor training procedure. Experiments on regression tasks on imaging and non-imaging input data show the method’s ability to generate diverse and multi-modal predictive distributions and how estimated uncertainty correlates with prediction error.

HAUNT

nnOOD: A Framework for Benchmarking Self-supervised Anomaly Localisation Methods. By isolating the synthetic, self-supervised task from the rest of the training process we perform a more faithful comparison of the tasks, whilst also making the workflow for evaluating over a given dataset quick and easy. Using this we have implemented the current state-of-the-art tasks and evaluated them on a challenging X-ray dataset.

意外的成功

别名

reception

Stochastic Weight Perturbations Along the Hessian: A Plug-and-Play Method to Compute Uncertainty of models by sampling along the eigen directions of the Hessian of the converged minimum. We demonstrate the utility of our methods on two challenging medical ultrasound imaging problems - cardiac view recognition and kidney segmentation.

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暗讽

Quantification of Predictive Uncertainty via Inference-Time Sampling that are trained to predict uncertainty estimates as variables of interest. These approaches require distinct architectural components and training mechanisms, may include restrictive assumptions and exhibit overconfidence, i.e., high confidence in imprecise predictions. In this work, we propose a

Hectic

Uncertainty Categories in Medical Image Segmentation: A Study of Source-Related Diversitylping build confidence with end users, and for improving the training and performance of the networks. Several different methods have been proposed to estimate uncertainties, including those from epistemic (relating to the model used) and aleatoric (relating to the data) sources using test-time drop

痛苦一生

色情

What Do Untargeted Adversarial Examples Reveal in Medical Image Segmentation?tion tasks regardless of the ground truth. To explore and identify the uncertain regions, we propose a post-training method with untargeted adversarial examples where the input image is iteratively perturbed in a direction that maximizes the loss of original and perturbed prediction. The perturbed p