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Quantification of Predictive Uncertainty via Inference-Time Sampling. https://t.co/24Bb6RmPc0
Uncertainty for Safe Utilization of Machine Learning in Medical Imaging
Overview of attention for book
Table of Contents
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Altmetric BadgeBook Overview
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Altmetric BadgeChapter 1 MOrphologically-Aware Jaccard-Based ITerative Optimization (MOJITO) for Consensus Segmentation
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Altmetric BadgeChapter 2 Quantification of Predictive Uncertainty via Inference-Time Sampling
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Altmetric BadgeChapter 3 How certain are your uncertainties?
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Altmetric BadgeChapter 4 On the Pitfalls of Entropy-Based Uncertainty for Multi-class Semi-supervised Segmentation
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Altmetric BadgeChapter 5 What Do Untargeted Adversarial Examples Reveal in Medical Image Segmentation?
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Altmetric BadgeChapter 6 Improved Post-hoc Probability Calibration for Out-of-Domain MRI Segmentation
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Altmetric BadgeChapter 7 Improving Error Detection in Deep Learning Based Radiotherapy Autocontouring Using Bayesian Uncertainty
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Altmetric BadgeChapter 8 Stochastic Weight Perturbations Along the Hessian: A Plug-and-Play Method to Compute Uncertainty
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Altmetric BadgeChapter 9 Calibration of Deep Medical Image Classifiers: An Empirical Comparison Using Dermatology and Histopathology Datasets
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Altmetric BadgeChapter 10 nnOOD: A Framework for Benchmarking Self-supervised Anomaly Localisation Methods
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Altmetric BadgeChapter 11 Generalized Probabilistic U-Net for Medical Image Segementation
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Altmetric BadgeChapter 12 Joint Paraspinal Muscle Segmentation and Inter-rater Labeling Variability Prediction with Multi-task TransUNet
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Altmetric BadgeChapter 13 Information Gain Sampling for Active Learning in Medical Image Classification