Abstract Machine learning methods strive to acquire a robust model during the training
process that can effectively generalize to test samples, even in the presence of distribution …

Test-time domain adaptation aims to adapt a source pre-trained model to a target domain
without using any source data. Existing works mainly consider the case where the target …

Fully test-time adaptation aims to adapt the network model based on sequential analysis of
input samples during the inference stage to address the cross-domain performance …

Domain Adaptation (DA) is important for deep learning-based medical image segmentation
models to deal with testing images from a new target domain. As the source-domain data …

Traditional test-time adaptation (TTA) methods face significant challenges in adapting to
dynamic environments characterized by continuously changing long-term target …

Abstract Continual Test-Time Adaptation (CTTA) generalizes conventional Test-Time
Adaptation (TTA) by assuming that the target domain is dynamic over time rather than …

In medical image segmentation, domain generalization poses a significant challenge due to
domain shifts caused by variations in data acquisition devices and other factors. These shifts …

One major problem in deep learning-based solutions for medical imaging is the drop in
performance when a model is tested on a data distribution different from the one that it is …

Existing motion forecasting models, while making progress, struggle to bridge the gap
between the source and target domains. Recent solutions often rely on an unrealistic …

Deep learning (DL) based methods have been extensively studied for medical image
segmentation, mostly emphasizing the design and training of DL networks. Only few …