The remarkable performance of the Transformer architecture in natural language processing
has recently also triggered broad interest in Computer Vision. Among other merits …

In recent years, Federated Learning (FL) has gained relevance in training collaborative
models without sharing sensitive data. Since its birth, Centralized FL (CFL) has been the …

We propose MM-Vet, an evaluation benchmark that examines large multimodal models
(LMMs) on complicated multimodal tasks. Recent LMMs have shown various intriguing …

As artificial intelligence (AI) rapidly approaches human-level performance in medical
imaging, it is crucial that it does not exacerbate or propagate healthcare disparities. Previous …

Few-shot learning (FSL) has emerged as an effective learning method and shows great
potential. Despite the recent creative works in tackling FSL tasks, learning valid information …

Artificial intelligence (AI) systems have increasingly achieved expert-level performance in
medical imaging applications. However, there is growing concern that such AI systems may …

Automated visual understanding of our diverse and open world demands computer vision
models to generalize well with minimal customization for specific tasks, similar to human …

This article discusses the opportunities, applications and future directions of large-scale
pretrained models, ie, foundation models, which promise to significantly improve the …

We introduce MedMNIST v2, a large-scale MNIST-like dataset collection of standardized
biomedical images, including 12 datasets for 2D and 6 datasets for 3D. All images are pre …

Abstract Machine-learning models for medical tasks can match or surpass the performance
of clinical experts. However, in settings differing from those of the training dataset, the …