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Found 6 papers, 5 papers with code
ViLMedic: a framework for research at the intersection of vision and language in medical AI
There is a growing need to model interactions between data modalities (e. g., vision, language) — both to improve AI predictions on existing tasks and to enable new applications.
CheXpert Plus: Hundreds of Thousands of Aligned Radiology Texts, Images and Patients
To address this, CheXpert Plus serves as a new collection of radiology data sources, made publicly available to enhance the scaling, performance, robustness, and fairness of models for all subsequent machine learning tasks in the field of radiology.
RoentGen: Vision-Language Foundation Model for Chest X-ray Generation
We present evidence that the resulting model (RoentGen) is able to create visually convincing, diverse synthetic CXR images, and that the output can be controlled to a new extent by using free-form text prompts including radiology-specific language.
Improving the Factual Correctness of Radiology Report Generation with Semantic Rewards
To overcome this limitation, we propose a new method, the RadGraph reward, to further improve the factual completeness and correctness of generated radiology reports.
Adapting Pretrained Vision-Language Foundational Models to Medical Imaging Domains
Multi-modal foundation models are typically trained on millions of pairs of natural images and text captions, frequently obtained through web-crawling approaches.
RadGraph: Extracting Clinical Entities and Relations from Radiology Reports
We release a development dataset, which contains board-certified radiologist annotations for 500 radiology reports from the MIMIC-CXR dataset (14, 579 entities and 10, 889 relations), and a test dataset, which contains two independent sets of board-certified radiologist annotations for 100 radiology reports split equally across the MIMIC-CXR and CheXpert datasets.
