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Decentralized federated learning: Fundamentals, state of the art, frameworks, trends, and challenges
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 …
models without sharing sensitive data. Since its birth, Centralized FL (CFL) has been the …
A comprehensive survey of few-shot learning: Evolution, applications, challenges, and opportunities
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 …
potential. Despite the recent creative works in tackling FSL tasks, learning valid information …
Mm-vet: Evaluating large multimodal models for integrated capabilities
We propose MM-Vet, an evaluation benchmark that examines large multimodal models
(LMMs) on complicated multimodal tasks. Recent LMMs have shown various intriguing …
(LMMs) on complicated multimodal tasks. Recent LMMs have shown various intriguing …
The limits of fair medical imaging AI in real-world generalization
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 …
imaging, it is crucial that it does not exacerbate or propagate healthcare disparities. Previous …
Robust and data-efficient generalization of self-supervised machine learning for diagnostic imaging
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 …
of clinical experts. However, in settings differing from those of the training dataset, the …
Pmc-vqa: Visual instruction tuning for medical visual question answering
Medical Visual Question Answering (MedVQA) presents a significant opportunity to enhance
diagnostic accuracy and healthcare delivery by leveraging artificial intelligence to interpret …
diagnostic accuracy and healthcare delivery by leveraging artificial intelligence to interpret …
Advances in medical image analysis with vision transformers: a comprehensive review
The remarkable performance of the Transformer architecture in natural language processing
has recently also triggered broad interest in Computer Vision. Among other merits …
has recently also triggered broad interest in Computer Vision. Among other merits …
Tinyvit: Fast pretraining distillation for small vision transformers
Vision transformer (ViT) recently has drawn great attention in computer vision due to its
remarkable model capability. However, most prevailing ViT models suffer from huge number …
remarkable model capability. However, most prevailing ViT models suffer from huge number …
RadImageNet: an open radiologic deep learning research dataset for effective transfer learning
Purpose To demonstrate the value of pretraining with millions of radiologic images
compared with ImageNet photographic images on downstream medical applications when …
compared with ImageNet photographic images on downstream medical applications when …
Deep learning approaches for data augmentation in medical imaging: a review
Deep learning has become a popular tool for medical image analysis, but the limited
availability of training data remains a major challenge, particularly in the medical field where …
availability of training data remains a major challenge, particularly in the medical field where …