Traditional biomedical artificial intelligence (AI) models, designed for specific tasks or
modalities, often exhibit limited flexibility in real-world deployment and struggle to utilize …

Personalized federated learning (PFL) is an exciting approach that allows machine learning
(ML) models to be trained on diverse and decentralized sources of data, while maintaining …

The transformer is primarily used in the field of natural language processing. Recently, it has
been adopted and shows promise in the computer vision (CV) field. Medical image analysis …

Semi-supervised learning (SSL) improves model generalization by leveraging massive
unlabeled data to augment limited labeled samples. However, currently, popular SSL …

Abstract Convolutional Neural Networks (CNNs) have advanced existing medical systems
for automatic disease diagnosis. However, there are still concerns about the reliability of …

Conventional task-and modality-specific artificial intelligence (AI) models are inflexible in
real-world deployment and maintenance for biomedicine. At the same time, the growing …

Foundation models trained on large-scale dataset gain a recent surge in CV and NLP. In
contrast, development in biomedical domain lags far behind due to data scarcity. To address …

Biomedical image classification is crucial for both computer vision tasks and clinical care.
The conventional method requires a significant amount of time and effort for extracting and …

Acquiring high-quality data for training discriminative models is a crucial yet challenging
aspect of building effective predictive systems. In this paper, we present Diffusion Inversion …

Following the explosive growth of global data, there is an ever-increasing demand for high-
throughput processing in image transmission systems. However, existing methods mainly …