Domain Adaptation and Representation Transfer [electronic resource] : 5th MICCAI Workshop, DART 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 12, 2023, Proceedings /

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.

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. .