Open-set domain adaptation is a developing and practical solution to training deep networks
using unlabeled data which have been collected among unknown data and are under …

Medical Image Analysis (MedIA) has become an essential tool in medicine and healthcare,
aiding in disease diagnosis, prognosis, and treatment planning, and recent successes in …

Convolutional neural networks (CNNs) have attracted much research attention and
achieved great improvements in single-image dehazing. However, previous learning-based …

Federated learning (FL) offers an effective privacy protection mechanism for cross-center
medical collaboration and data sharing. In multi-site medical image segmentation, FL allows …

While deep neural networks (DNNs) have revolutionized many fields, their fragility to
carefully designed adversarial attacks impedes the usage of DNNs in safety-critical …

Domain generalization (DG) is one of the critical issues for deep learning in unknown
domains. How to effectively represent domain-invariant context (DIC) is a difficult problem …

White blood cell (WBC) morphology examination plays a crucial role in diagnosing many
diseases. One of the most important steps in WBC morphology analysis is WBC image …

Generalization is an important attribute of machine learning models, particularly for those
that are to be deployed in a medical context, where unreliable predictions can have real …

Medical image analysis (MedIA) has become an essential tool in medicine and healthcare,
aiding in disease diagnosis, prognosis, and treatment planning, and recent successes in …

As a main branch of domain adaptation (DA), multi-source DA (MSDA) has attracted
increasing attention for exploiting information from multi-source domain data. However, how …