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 /

Contributor(s):

Material type: Text

TextSeries: Lecture Notes in Computer Science ; 14291Publisher: Cham : Springer Nature Switzerland : Imprint: Springer, 2023Edition: 1st ed. 2023Description: XIII, 220 p. 58 illus., 54 illus. in color. online resourceContent type:

text

Media type:

computer

Carrier type:

online resource

ISBN:

9783031443367

Subject(s):

Additional physical formats: Printed edition:: No title; Printed edition:: No titleDDC classification:

006.3 23

LOC classification:

Q334-342
TA347.A78

Online resources:

Click here to access online

Contents:

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

In: Springer Nature eBookSummary: 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.

Tags from this library: No tags from this library for this title. Log in to add tags.

Average rating: 0.0 (0 votes)

No physical items for this record

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.

There are no comments on this title.

to post a comment.