國立高雄大學圖資館 |

Language: English

Back

Domain adaptation and representation transfer4th MICCAI Workshop, DART 2022, held in conjunction with MICCAI 2022, Singapore, September 22, 2022 : proceedings /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Domain adaptation and representation transferedited by Konstantinos Kamnitsas ... [et al.].
Reminder of title:	4th MICCAI Workshop, DART 2022, held in conjunction with MICCAI 2022, Singapore, September 22, 2022 : proceedings /
remainder title:	DART 2022
other author:	Kamnitsas, Konstantinos.
corporate name:
Published:	Cham :Springer Nature Switzerland :2022.
Description:	x, 147 p. :ill. (chiefly color), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Diagnostic imagingCongresses.Data processing
Online resource:	https://doi.org/10.1007/978-3-031-16852-9
ISBN:	9783031168529$q(electronic bk.)

Domain adaptation and representation transfer4th 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 ;4891..

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$q(electronic bk.)

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

445765
Diagnostic imaging
--Data processing--Congresses.

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

Dewey Class. No.: 616.0754

Domain adaptation and representation transfer4th MICCAI Workshop, DART 2022, held in conjunction with MICCAI 2022, Singapore, September 22, 2022 : proceedings /
LDR:02952nmm a2200361 a 4500 001 630364
003 DE-He213
005 20220920000824.0
006 m d
007 cr nn 008maaau
008 230329s2022 sz s 0 eng d
020 $a 9783031168529$q(electronic bk.)
020 $a 9783031168512$q(paper)
024 7 $a 10.1007/978-3-031-16852-9 $2 doi
035 $a 978-3-031-16852-9
040 $a GP $c GP
041 0 $a eng
050 4 $a RC78.7.D53 $b D65 2022
072 7 $a UYQV $2 bicssc
072 7 $a COM012000 $2 bisacsh
072 7 $a UYQV $2 thema
082 0 4 $a 616.0754 $2 23
090 $a RC78.7.D53 $b D666 2022
111 2 $n (3rd : $d 1998 : $c Amsterdam, Netherlands) $3 194767
245 1 0 $a Domain adaptation and representation transfer $h [electronic resource] : $b 4th MICCAI Workshop, DART 2022, held in conjunction with MICCAI 2022, Singapore, September 22, 2022 : proceedings / $c edited by Konstantinos Kamnitsas ... [et al.].
246 3 $a DART 2022
246 3 $a MICCAI 2022
260 $a Cham : $b Springer Nature Switzerland : $b Imprint: Springer, $c 2022.
300 $a x, 147 p. : $b ill. (chiefly color), digital ; $c 24 cm.
490 1 $a Lecture notes in computer science, $x 0302-9743 ; $v 13542
505 0 $a 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.
520 $a 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.
650 0 $a Diagnostic imaging $x Data processing $v Congresses. $3 445765
650 0 $a Artificial intelligence $x Medical applications $v Congresses. $3 442992
650 1 4 $a Computer Vision. $3 913138
650 2 4 $a Computer Engineering and Networks. $3 913124
650 2 4 $a Machine Learning. $3 833608
650 2 4 $a Computing Milieux. $3 275270
650 2 4 $a Computer and Information Systems Applications. $3 913125
700 1 $a Kamnitsas, Konstantinos. $3 935520
710 2 $a SpringerLink (Online service) $3 273601
711 2 $n (3rd : $d 1998 : $c Amsterdam, Netherlands) $3 194767
773 0 $t Springer Nature eBook
830 0 $a Lecture notes in computer science ; $v 4891. $3 383229
856 4 0 $u https://doi.org/10.1007/978-3-031-16852-9
950 $a Computer Science (SpringerNature-11645)