REVIEW 3 cited by
EVA-X: A Foundation Model for General Chest X-ray Analysis with Self-supervised Learning
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
The diagnosis and treatment of chest diseases play a crucial role in maintaining human health. X-ray examination has become the most common clinical examination means due to its efficiency and cost-effectiveness. Artificial intelligence analysis methods for chest X-ray images are limited by insufficient annotation data and varying levels of annotation, resulting in weak generalization ability and difficulty in clinical dissemination. Here we present EVA-X, an innovative foundational model based on X-ray images with broad applicability to various chest disease detection tasks. EVA-X is the first X-ray image based self-supervised learning method capable of capturing both semantic and geometric information from unlabeled images for universal X-ray image representation. Through extensive experimentation, EVA-X has demonstrated exceptional performance in chest disease analysis and localization, becoming the first model capable of spanning over 20 different chest diseases and achieving leading results in over 11 different detection tasks in the medical field. Additionally, EVA-X significantly reduces the burden of data annotation in the medical AI field, showcasing strong potential in the domain of few-shot learning. The emergence of EVA-X will greatly propel the development and application of foundational medical models, bringing about revolutionary changes in future medical research and clinical practice. Our codes and models are available at: https://github.com/hustvl/EVA-X.
Forward citations
Cited by 3 Pith papers
-
M-SpecGene: Generalized Foundation Model for RGBT Multispectral Vision
M-SpecGene is a Siamese masked-autoencoder foundation model for RGB-thermal vision, trained on the RGBT550K dataset with a GMM-CMSS progressive masking strategy, and evaluated on four downstream tasks.
-
CX-Mind: A Pioneering Multimodal Large Language Model for Interleaved Reasoning in Chest X-ray via Curriculum-Guided Reinforcement Learning
CX-Mind combines curriculum reinforcement learning and rule-based process rewards to train a chest X-ray vision-language model that produces interleaved think-answer reasoning and reports state-of-the-art results acro...
-
Chest X-ray Foundation Model with Global and Local Representations Integration
CheXFound, a ViT-Large model pretrained on 987K CXRs with DINOv2 plus the GLoRI head, outperforms prior CXR foundation models on long-tailed disease classification and transfer tasks.
Discussion (0). Continue with ORCID to comment.