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020 _a9783031458576
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024 7 _a10.1007/978-3-031-45857-6
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050 4 _aTA1501-1820
050 4 _aTA1634
072 7 _aUYT
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072 7 _aCOM016000
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245 1 0 _aDomain Adaptation and Representation Transfer
_h[electronic resource] :
_b5th MICCAI Workshop, DART 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 12, 2023, Proceedings /
_cedited by Lisa Koch, M. Jorge Cardoso, Enzo Ferrante, Konstantinos Kamnitsas, Mobarakol Islam, Meirui Jiang, Nicola Rieke, Sotirios A. Tsaftaris, Dong Yang.
250 _a1st ed. 2024.
264 1 _aCham :
_bSpringer Nature Switzerland :
_bImprint: Springer,
_c2024.
300 _aX, 170 p. 44 illus., 40 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
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338 _aonline resource
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347 _atext file
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490 1 _aLecture Notes in Computer Science,
_x1611-3349 ;
_v14293
505 0 _aDomain adaptation of MRI scanners as an alternative to MRI harmonization -- MultiVT: Multiple-Task Framework for Dentistry -- Black-Box Unsupervised Domain Adaptation for Medical Image Segmentation -- PLST: A Pseudo-Labels with a Smooth Transition Strategy for Medical Site Adaptation -- Compositional Representation Learning for Brain Tumor Segmentation -- Hierarchical Compositionality in Hyperbolic Space for Robust Medical Image Segmentation -- Realistic Data Enrichment for Robust Image Segmentation in Kidney Transplant Pathology -- Boosting Knowledge Distillation via Random Fourier Features for Prostate Cancer Grading in Histopathology Images -- Semi-supervised Domain Adaptation for Automatic Quality Control of FLAIR MRIs in a Clinical Data Warehouse -- Towards Foundation Models Learned from Anatomy in Medical Imaging via Self-Supervision -- The Performance of Transferability Metrics does not Translate to Medical Tasks -- DGM-DR: Domain Generalization with Mutual Information Regularized Diabetic Retinopathy Classification -- SEDA: Self-Ensembling ViT with Defensive Distillation and Adversarial Training for robust Chest X-rays Classification -- A Continual Learning Approach for Cross-Domain White Blood Cell Classification -- Metadata Improves Segmentation Through Multitasking Elicitation -- Self-Prompting Large Vision Models for Few-Shot Medical Image Segmentation.
520 _aThis book constitutes the refereed proceedings of the 5th MICCAI Workshop on Domain Adaptation and Representation Transfer, DART 2023, which was held in conjunction with MICCAI 2023, in October 2023. The 16 full papers presented in this book were carefully reviewed and selected from 32 submissions. They discuss methodological advancements and ideas that can improve the applicability of machine learning (ML)/deep learning (DL) approaches to clinical setting by making them robust and consistent across different domains. .
650 0 _aImage processing
_xDigital techniques.
_964228
650 0 _aComputer vision.
650 0 _aApplication software.
650 0 _aMachine learning.
_955059
650 0 _aComputers.
_965793
650 0 _aInformation technology
_xManagement.
_957223
650 1 4 _aComputer Imaging, Vision, Pattern Recognition and Graphics.
_965550
650 2 4 _aComputer and Information Systems Applications.
650 2 4 _aMachine Learning.
_955059
650 2 4 _aComputing Milieux.
_966530
650 2 4 _aComputer Application in Administrative Data Processing.
_966531
700 1 _aKoch, Lisa.
_eeditor.
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_4http://id.loc.gov/vocabulary/relators/edt
_966532
700 1 _aCardoso, M. Jorge.
_eeditor.
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_4http://id.loc.gov/vocabulary/relators/edt
_966533
700 1 _aFerrante, Enzo.
_eeditor.
_0(orcid)0000-0002-8500-788X
_1https://orcid.org/0000-0002-8500-788X
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_966534
700 1 _aKamnitsas, Konstantinos.
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_966535
700 1 _aIslam, Mobarakol.
_eeditor.
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_966536
700 1 _aJiang, Meirui.
_eeditor.
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_966537
700 1 _aRieke, Nicola.
_eeditor.
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_966538
700 1 _aTsaftaris, Sotirios A.
_eeditor.
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_966539
700 1 _aYang, Dong.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_966540
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031458569
776 0 8 _iPrinted edition:
_z9783031458583
830 0 _aLecture Notes in Computer Science (LNCS)
_g(Springer, Berlin)
_x1611-3349 ;
_v14293
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856 4 0 _uhttps://doi.org/10.1007/978-3-031-45857-6
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