Uncertainty for Safe Utilization of Machine Learning in Medical Imaging

4th International Workshop, UNSURE 2022, Held in Conjunction with MICCAI 2022, Singapore, September 18, 2022, Proceedings

Häftad, Engelska, 2022

Av Carole H. Sudre, Christian F. Baumgartner, Adrian Dalca, Chen Qin, Ryutaro Tanno, Koen Van Leemput, William M. Wells III, Koen van Leemput

809 kr

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This book constitutes the refereed proceedings of the Fourth Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, UNSURE 2022, held in conjunction with MICCAI 2022.

Produktinformation

Utgivningsdatum2022-09-18
Mått155 x 235 x 9 mm
Vikt254 g
FormatHäftad
SpråkEngelska
SerieLecture Notes in Computer Science
Antal sidor147
FörlagSpringer International Publishing AG
ISBN9783031167485

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Tillämpad datateknik inom Data och it
Informationsteknik: allmänt inom Data och it
Systemvetenskap och AI inom Data och it
Artificiell intelligens inom Data och it

Uncertainty Modelling.- MOrphologically-aware Jaccard-based ITerative Optimization (MOJITO) for Consensus Segmentation.- Quantification of Predictive Uncertainty via Inference-Time Sampling.- Uncertainty categories in medical image segmentation: a study of source-related diversity..- On the pitfalls of entropy-based uncertainty for multi-class semi-supervised segmentation.- What Do Untargeted Adversarial Examples Reveal In Medical Image Segmentation?..- Uncertainty calibration.- Improved post-hoc probability calibration for out-of-domain MRI segmentation..- Improving error detection in deep learning-based radiotherapy autocontouring using Bayesian uncertainty.- A Plug-and-Play Method to Compute Uncertainty.- Calibration of Deep Medical Image Classifiers: An Empirical Comparison using Dermatology and Histopathology Datasets.- Annotation uncertainty and out of distribution management.- nnOOD: A Framework for Benchmarking Self-supervised Anomaly Localisation Methods.- Generalized Probabilistic U-Net for medical image segmentation.- Joint paraspinal muscle segmentation and inter-rater labeling variability prediction with multi-task TransUNet.- Information Gain Sampling for Active Learning in Medical Image Classification.

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