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024 7 _a10.1007/978-3-031-16852-9
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082 0 4 _a006.37
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245 1 0 _aDomain Adaptation and Representation Transfer
_h[electronic resource] :
_b4th MICCAI Workshop, DART 2022, Held in Conjunction with MICCAI 2022, Singapore, September 22, 2022, Proceedings /
_cedited by Konstantinos Kamnitsas, Lisa Koch, Mobarakol Islam, Ziyue Xu, Jorge Cardoso, Qi Dou, Nicola Rieke, Sotirios Tsaftaris.
250 _a1st ed. 2022.
264 1 _aCham :
_bSpringer Nature Switzerland :
_bImprint: Springer,
_c2022.
300 _aX, 147 p. 50 illus., 46 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
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338 _aonline resource
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490 1 _aLecture Notes in Computer Science,
_x1611-3349 ;
_v13542
505 0 _aDetecting Melanoma Fairly: Skin Tone Detection and Debiasing for Skin Lesion Classification -- Benchmarking Transformers for Medical Image Classification -- Supervised domain adaptation using gradients transfer for improved medical image analysis -- Stain-AgLr: Stain Agnostic Learning for Computational Histopathology using Domain Consistency and Stain Regeneration Loss -- MetaMedSeg: Volumetric Meta-learning for Few-Shot Organ Segmentation -- Unsupervised site adaptation by intra-site variability alignment -- Discriminative, Restorative, and Adversarial Learning: Stepwise Incremental Pretraining -- POPAR: Patch Order Prediction and Appearance Recovery for Self-supervised Medical Image Analysis -- Feather-Light Fourier Domain Adaptation in Magnetic Resonance Imaging -- Seamless Iterative Semi-Supervised Correction of Imperfect Labels in Microscopy Images -- Task-agnostic Continual Hippocampus Segmentation for Smooth Population Shifts -- Adaptive Optimization with Fewer Epochs Improves Across-Scanner Generalization of U-Net based Medical Image Segmentation -- CateNorm: Categorical Normalization for Robust Medical Image Segmentation.
520 _aThis book constitutes the refereed proceedings of the 4th MICCAI Workshop on Domain Adaptation and Representation Transfer, DART 2022, held in conjunction with MICCAI 2022, in September 2022. DART 2022 accepted 13 papers from the 25 submissions received. The workshop aims at creating a discussion forum to compare, evaluate, and 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 _aComputer vision.
650 0 _aComputer engineering.
650 0 _aComputer networks .
650 0 _aMachine learning.
650 0 _aComputers.
650 0 _aApplication software.
650 1 4 _aComputer Vision.
650 2 4 _aComputer Engineering and Networks.
650 2 4 _aMachine Learning.
650 2 4 _aComputing Milieux.
650 2 4 _aComputer and Information Systems Applications.
700 1 _aKamnitsas, Konstantinos.
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700 1 _aKoch, Lisa.
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700 1 _aIslam, Mobarakol.
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700 1 _aXu, Ziyue.
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700 1 _aCardoso, Jorge.
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700 1 _aDou, Qi.
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700 1 _aRieke, Nicola.
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700 1 _aTsaftaris, Sotirios.
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710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
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776 0 8 _iPrinted edition:
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830 0 _aLecture Notes in Computer Science,
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856 4 0 _uhttps://doi.org/10.1007/978-3-031-16852-9
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