核心概念
Proposing the ECAMP framework for entity-centered context-aware medical vision-language pre-training.
摘要
The ECAMP framework addresses the entity-specific context within radiology reports and enhances the interplay between text and image modalities. It distills entity-centered context from medical reports, refines contextual relationships, and improves downstream task performance. By incorporating components like entity-aware context distillation, context-enhanced masked language modeling, and multi-scale context fusion, ECAMP establishes a new standard in cross-modality learning for medical imaging.
統計資料
Extensive experiments conducted on various tasks including classification, segmentation, and detection.
Performance leaps over current state-of-the-art methods demonstrated.
Code and models available at https://github.com/ToniChopp/ECAMP.
引述
"ECAMP significantly refines the interplay between text and image modalities."
"Our proposed multi-scale context fusion design improves semantic integration for better performance."
"Combining these components leads to significant performance leaps over current state-of-the-art methods."