Due to the cross-domain distribution shift aroused from diverse medical imaging systems,
many deep learning segmentation methods fail to perform well on unseen data, which limits …

The domain gap caused mainly by variable medical image quality renders a major obstacle
on the path between training a segmentation model in the lab and applying the trained …

Deep neural network has demonstrated high-level accuracy in rail surface defect
segmentation. However, deploying these deep models in actual inspection situations results …

Introduction Brain medical image segmentation is a critical task in medical image
processing, playing a significant role in the prediction and diagnosis of diseases such as …

The topic of generalizing machine learning models learned on a collection of source
domains to unknown target domains is challenging. While many domain generalization (DG) …

Surface quality control is a crucial part of rail manufacturing. Deep neural networks have
shown impressive accuracy in rail surface defect segmentation under the assumption that …

Deep learning techniques have been applied to medical image segmentation and
demonstrated expert‐level performance. Due to the poor generalization abilities of the …

Abstract Domain Adaptation (DA) methods are widely used in medical image segmentation
tasks to tackle the problem of differently distributed train (source) and test (target) data. We …

Abstract White Matter Hyperintensities (WMH) are important neuroradiological markers of
small vessel disease in brain MRI, with automatic segmentation tasks essential in research …

The domain gap caused mainly by variable medical image quality renders a major obstacle
on the path between training a segmentation model in the lab and applying the trained …