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

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Improved Post-hoc Probability Calibration for Out-of-Domain MRI Segmentationdeep networks interpretable, by aligning prediction probability with the actual accuracy in test data. In image segmentation, well-calibrated probabilities allow radiologists to identify regions where model-predicted segmentations are unreliable. These unreliable predictions often occur to out-of-do

大酒杯

Improving Error Detection in Deep Learning Based Radiotherapy Autocontouring Using Bayesian Uncertaiond to inaccurate regions. To help speed up the mandatory quality assessment (QA) of contours in radiotherapy, these heatmaps could be used as stimuli to direct visual attention of clinicians to potential inaccuracies. In practice, this is non-trivial to achieve since many accurate regions also exhi

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Stochastic Weight Perturbations Along the Hessian: A Plug-and-Play Method to Compute Uncertaintys. The predominant method to generating uncertainty scores is to utilize a Bayesian formulation of deep learning. In this paper, we present a plug-and-play method to obtain samples from an already optimized model. Specifically, we present a simple, albeit principled methodology, to generate a number

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Calibration of Deep Medical Image Classifiers: An Empirical Comparison Using Dermatology and Histopa Mis-calibration is the deviation between predictive probability (confidence) and classification correctness. Well-calibrated classifiers enable cost-sensitive and selective decision-making. This paper presents an empirical investigation of calibration methods on two medical image datasets (multi-cl

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Generalized Probabilistic U-Net for Medical Image Segementationas the latent space distribution that can better approximate the uncertainty in the reference segmentations. We study the effect the choice of latent space distribution has on capturing the uncertainty in the reference segmentations using the LIDC-IDRI dataset. We show that the choice of distributio

流利圆滑

Joint Paraspinal Muscle Segmentation and Inter-rater Labeling Variability Prediction with Multi-taskrly understood musculoskeletal disorder in adults. Accurate paraspinal muscle segmentation from MRI is crucial to enable new image-based biomarkers for the diagnosis and prognosis of LBP. Manual segmentation is laborious and time-consuming. In addition, high individual anatomical variations also pos

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Information Gain Sampling for Active Learning in Medical Image Classificationing large datasets. Unlabelled datasets are easier to obtain, and in many contexts, it would be feasible for an expert to provide labels for a small subset of images. This work presents an information-theoretic active learning framework that guides the optimal selection of images from the unlabelled

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