Deep learning (DL) has shown remarkable success in various medical imaging data
analysis applications. However, it remains challenging for DL models to achieve good …

Synthetic aperture radar (SAR) target recognition is a fundamental task in SAR image
interpretation, which has made tremendous progress with the advancement of artificial …

Deep neural networks are commonly used for automated medical image segmentation, but
models will frequently struggle to generalize well across different imaging modalities. This …

Automatic segmentation of medical images has been a hot research topic in the field of deep
learning in recent years, and achieving accurate segmentation of medical images is …

Partial domain adaptation, which aims to transfer knowledge from a source domain with rich
labels to a unlabeled target domain where target class space is a subspace of source class …

Abstract Machine learning (ML) applications in medical artificial intelligence (AI) systems
have shifted from traditional and statistical methods to increasing application of deep …

Unsupervised domain adaptation (UDA) is used to extend the model working on well-
annotated source data to unlabeled target data. However, in practice, due to privacy and …

Automaticlesion segmentation is important for assisting doctors in the diagnostic process.
Recent deep learning approaches heavily rely on large-scale datasets, which are difficult to …

Abstract Unsupervised Domain Adaptation (UDA) leverages a labeled source domain to
solve tasks in an unlabeled target domain. While Transformer-based methods have shown …

Accurate segmentation of organelle instances from electron microscopy (EM) images plays
an essential role in many neuroscience researches. However, practical scenarios usually …