BiomedCLIP, pretrained on the new 15-million-pair PMC-15M dataset, achieves state-of-the-art performance on diverse biomedical vision-language tasks and even outperforms radiology-specific models on chest X-ray pneumonia detection.
Title resolution pending
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
roles
background 1polarities
background 1representative citing papers
Dual-wavelength incoherent multiplexing enables native signed optical multiplication with constant overhead on lithium niobate, validated by 40 GHz bandwidth, 1.27% error, and neural network accuracies of 95.1% on Moons and 91.63% on MNIST.
TRON demonstrates a trainable and reconfigurable optical neural network that combines multi-scattering media with DMD-based matrix multiplication and performs in-situ optimization plus neural architecture search on the optical hardware itself.
citing papers explorer
-
BiomedCLIP: a multimodal biomedical foundation model pretrained from fifteen million scientific image-text pairs
BiomedCLIP, pretrained on the new 15-million-pair PMC-15M dataset, achieves state-of-the-art performance on diverse biomedical vision-language tasks and even outperforms radiology-specific models on chest X-ray pneumonia detection.
-
Scalable native signed optical computing enabled by dual-wavelength incoherent multiplexing
Dual-wavelength incoherent multiplexing enables native signed optical multiplication with constant overhead on lithium niobate, validated by 40 GHz bandwidth, 1.27% error, and neural network accuracies of 95.1% on Moons and 91.63% on MNIST.
-
TRON: Trainable, architecture-reconfigurable random optical neural networks
TRON demonstrates a trainable and reconfigurable optical neural network that combines multi-scattering media with DMD-based matrix multiplication and performs in-situ optimization plus neural architecture search on the optical hardware itself.