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E-raamat: Domain Adaptation and Representation Transfer: 5th MICCAI Workshop, DART 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 12, 2023, Proceedings

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Domain Adaptation and Representation Transfer: 5th MICCAI Workshop, DART 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 12, 2023, ProceedingsSuurem pilt
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This 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.

 

Domain 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.
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