Uncertainty for Safe Utilization of Machine Learning in Medical Imaging [electronic resource] : 5th International Workshop, UNSURE 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 12, 2023, Proceedings / edited by Carole H. Sudre, Christian F. Baumgartner, Adrian Dalca, Raghav Mehta, Chen Qin, William M. Wells.

Contributor(s):

Material type: Text

TextLanguage: English Series: Publication details: Cham : Springer Nature Switzerland : Imprint: Springer, 2023.Edition: 1st ed. 2023Description: XIII, 220 p. 58 illus., 54 illus. in color. online resourceISBN:

9783031443367

Subject(s):

DDC classification:

006.3 23

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Contents:

Uncertainty estimation and modelling -- Out of Distribution management and domain shift robustness -- Bayesian deep learning and uncertainty calibration.

Summary: This book constitutes the refereed proceedings of the 5th Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, UNSURE 2023, held in conjunction with MICCAI 2023 in Vancouver, Canada, in October 2023. For this workshop, 21 papers from 32 submissions were accepted for publication. The accepted papers cover the fields of uncertainty estimation and modeling, as well as out of distribution management, domain shift robustness, Bayesian deep learning and uncertainty calibration.

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Holdings
Item type	Current library	Call number	Materials specified	Status	Date due	Barcode	Item holds
E-Books	National Library of India Online Resource	006.3 (Browse shelf(Opens below))		Available		EBK000047788ENG

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Uncertainty estimation and modelling -- Out of Distribution management and domain shift robustness -- Bayesian deep learning and uncertainty calibration.

This book constitutes the refereed proceedings of the 5th Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, UNSURE 2023, held in conjunction with MICCAI 2023 in Vancouver, Canada, in October 2023. For this workshop, 21 papers from 32 submissions were accepted for publication. The accepted papers cover the fields of uncertainty estimation and modeling, as well as out of distribution management, domain shift robustness, Bayesian deep learning and uncertainty calibration.

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