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Domain adaptation and representation...

Domain Adaptation and Representation Transfer (Workshop) (2022 :)

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Domain adaptation and representation transfer = 4th MICCAI Workshop, DART 2022, held in conjunction with MICCAI 2022, Singapore, September 22, 2022 : proceedings /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Domain adaptation and representation transfer/ edited by Konstantinos Kamnitsas ... [et al.].
其他題名:	4th MICCAI Workshop, DART 2022, held in conjunction with MICCAI 2022, Singapore, September 22, 2022 : proceedings /
其他題名:	DART 2022
其他作者:	Kamnitsas, Konstantinos.
團體作者:	Domain Adaptation and Representation Transfer (Workshop)
出版者:	Cham :Springer Nature Switzerland : : 2022.,
面頁冊數:	x, 147 p. :ill. (chiefly color), digital ;24 cm.
內容註:	Detecting 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.
Contained By:	Springer Nature eBook
標題:	Diagnostic imaging - Congresses. - Data processing -
電子資源:	https://doi.org/10.1007/978-3-031-16852-9
ISBN:	9783031168529

Domain adaptation and representation transfer = 4th MICCAI Workshop, DART 2022, held in conjunction with MICCAI 2022, Singapore, September 22, 2022 : proceedings /
Domain adaptation and representation transfer4th MICCAI Workshop, DART 2022, held in conjunction with MICCAI 2022, Singapore, September 22, 2022 : proceedings /[electronic resource] :DART 2022edited by Konstantinos Kamnitsas ... [et al.]. - Cham :Springer Nature Switzerland :2022. - x, 147 p. :ill. (chiefly color), digital ;24 cm. - Lecture notes in computer science,135420302-9743 ;. - Lecture notes in computer science ;13542..

Detecting 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.

This 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.

ISBN: 9783031168529

Standard No.: 10.1007/978-3-031-16852-9doiSubjects--Topical Terms:

893542
Diagnostic imaging
--Data processing--Congresses.

LC Class. No.: RC78.7.D53 / D65 2022

Dewey Class. No.: 616.0754

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