Domain Adaptation and Representation Transfer

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.

Inhalt

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.