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024 7 _a10.1007/978-3-031-16749-2
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050 4 _aQ334-342
050 4 _aTA347.A78
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072 7 _aCOM004000
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245 1 0 _aUncertainty for Safe Utilization of Machine Learning in Medical Imaging
_h[electronic resource] :
_b4th International Workshop, UNSURE 2022, Held in Conjunction with MICCAI 2022, Singapore, September 18, 2022, Proceedings /
_cedited by Carole H. Sudre, Christian F. Baumgartner, Adrian Dalca, Chen Qin, Ryutaro Tanno, Koen Van Leemput, William M. Wells III.
250 _a1st ed. 2022.
264 1 _aCham :
_bSpringer Nature Switzerland :
_bImprint: Springer,
_c2022.
300 _aX, 147 p. 39 illus., 32 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
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490 1 _aLecture Notes in Computer Science,
_x1611-3349 ;
_v13563
505 0 _aUncertainty 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.
520 _aThis 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. The conference was hybrid event held from Singapore. For this workshop, 13 papers from 22 submissions were accepted for publication. They focus on developing awareness and encouraging research in the field of uncertainty modelling to enable safe implementation of machine learning tools in the clinical world.
650 0 _aArtificial intelligence.
650 0 _aImage processing
_xDigital techniques.
650 0 _aComputer vision.
650 0 _aComputers.
650 0 _aApplication software.
650 1 4 _aArtificial Intelligence.
650 2 4 _aComputer Imaging, Vision, Pattern Recognition and Graphics.
650 2 4 _aComputing Milieux.
650 2 4 _aComputer and Information Systems Applications.
700 1 _aSudre, Carole H.
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700 1 _aBaumgartner, Christian F.
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700 1 _aDalca, Adrian.
_eeditor.
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700 1 _aQin, Chen.
_eeditor.
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700 1 _aTanno, Ryutaro.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aVan Leemput, Koen.
_eeditor.
_4edt
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700 1 _aWells III, William M.
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710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031167485
776 0 8 _iPrinted edition:
_z9783031167508
830 0 _aLecture Notes in Computer Science,
_x1611-3349 ;
_v13563
856 4 0 _uhttps://doi.org/10.1007/978-3-031-16749-2
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