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

偏狂症

On the Pitfalls of Entropy-Based Uncertainty for Multi-class Semi-supervised Segmentationed solution on a challenging multi-class segmentation dataset and in two well-known uncertainty-based segmentation methods. The reported results demonstrate that by simply replacing the mechanism used to compute the uncertainty, our proposed solution brings consistent improvements.

Conjuction

What Do Untargeted Adversarial Examples Reveal in Medical Image Segmentation?sults for uncertain region findings on medical image datasets while only requiring one extra inference from a pre-trained model and short iteration of attack. We expect our novel findings can provide insights for future medical image segmentation problems where detection of subtle variations (e.g.,

Functional

Improved Post-hoc Probability Calibration for Out-of-Domain MRI Segmentationodel is tested on cardiac MRI segmentation datasets that contain unseen imaging artifacts and images from an unseen imaging protocol. We demonstrate reduced calibration errors compared with the state-of-the-art calibration algorithm.

官僚统治

Trigger-Point

AGONY

Joint Paraspinal Muscle Segmentation and Inter-rater Labeling Variability Prediction with Multi-taskdicting inter-rater labeling variability visualized using a variance map of three raters’ annotations. Our technique is validated on MRIs of paraspinal muscles at four different disc levels from 118 LBP patients. Benefiting from the transformer mechanism and convolution neural networks, our algorith

cochlea

Information Gain Sampling for Active Learning in Medical Image Classificationes including the diversity based CoreSet and uncertainty based maximum entropy sampling. Specifically, AEIG achieves . of overall performance with only 19% of the training data, while other active learning approaches require around 25%. We show that, by careful design choices, our model can be integ

interpose

彻底明白

Simulate