Model optimization techniques in personalized federated learning: A survey
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 …
(ML) models to be trained on diverse and decentralized sources of data, while maintaining …
Recent progress in transformer-based medical image analysis
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 …
been adopted and shows promise in the computer vision (CV) field. Medical image analysis …
A generalist vision–language foundation model for diverse biomedical tasks
Traditional biomedical artificial intelligence (AI) models, designed for specific tasks or
modalities, often exhibit limited flexibility in real-world deployment and struggle to utilize …
modalities, often exhibit limited flexibility in real-world deployment and struggle to utilize …
Usb: A unified semi-supervised learning benchmark for classification
Semi-supervised learning (SSL) improves model generalization by leveraging massive
unlabeled data to augment limited labeled samples. However, currently, popular SSL …
unlabeled data to augment limited labeled samples. However, currently, popular SSL …
MedViT: a robust vision transformer for generalized medical image classification
Abstract Convolutional Neural Networks (CNNs) have advanced existing medical systems
for automatic disease diagnosis. However, there are still concerns about the reliability of …
for automatic disease diagnosis. However, there are still concerns about the reliability of …
Biomedgpt: A unified and generalist biomedical generative pre-trained transformer for vision, language, and multimodal tasks
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 …
real-world deployment and maintenance for biomedicine. At the same time, the growing …
Pmc-clip: Contrastive language-image pre-training using biomedical documents
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 …
contrast, development in biomedical domain lags far behind due to data scarcity. To address …
Training on thin air: Improve image classification with generated data
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 …
aspect of building effective predictive systems. In this paper, we present Diffusion Inversion …
A quantum convolutional network and ResNet (50)-based classification architecture for the MNIST medical dataset
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 …
The conventional method requires a significant amount of time and effort for extracting and …
Photonic unsupervised learning variational autoencoder for high-throughput and low-latency image transmission
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 …
throughput processing in image transmission systems. However, existing methods mainly …