Most recent test-time adaptation methods focus on only classification tasks, use specialized
network architectures, destroy model calibration or rely on lightweight information from the …

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 …

Developing computational pathology models is essential for reducing manual tissue typing
from whole slide images, transferring knowledge from the source domain to an unlabeled …

Test-time augmentation (TTA) is typically used in image classification tasks to improve the
classification accuracy of convolutional neural networks (CNNs). In the inference phase, TTA …

There is a strong incentive to develop computational pathology models to i) ease the burden
of tissue typology annotation from whole slide histological images; ii) transfer knowledge …

Unsupervised domain adaptation (UDA) in medical image segmentation aims to improve the
generalization of deep models by alleviating domain gaps caused by inconsistency across …

Medical Image Analysis (MedIA) has become indispensable in modern healthcare,
enhancing clinical diagnostics and personalized treatment. Despite the remarkable …

Data augmentation is known to contribute significantly to the robustness of machine learning
models. In most instances, data augmentation is utilized during the training phase. Test …

Despite that deep learning models have achieved remarkable performance in medical
image segmentation, their performance is often limited on testing images from new centers …

Magnetic Resonance Imaging (MRI) is considered the gold standard of medical imaging
because of the excellent soft-tissue contrast exhibited in the images reconstructed by the …