The Langlotz Lab
The Langlotz laboratory is focused on the development and application of machine learning and other innovative computational and analytical methods to accelerate disease detection and eliminate diagnostic errors.
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Foundation Models
CheXagent: Towards a Foundation Model for Chest X-Ray Interpretation
Self-Supervised Learning
ViLLA: Fine-Grained Vision-Language Representation Learning from Real-World Data
Generative Models
RoentGen: Vision-Language Foundation Model for Chest X-ray Generation
Radiology Report Summarization
Toward Expanding the Scope of Radiology Report Summarization to Multiple Anatomies and Modalities
Radiology Report Summarization
Overview of the RadSum23 Shared Task on Multi-modal and Multi-anatomical Radiology Report Summarization
Radiology Report Generation
Improving the Factual Correctness of Radiology Report Generation with Semantic Rewards
Knowledge Graph Extraction
Extracting Clinical Entities and Relations to Form Knowledge Graphs from Radiology Reports
Contrastive Pre-Training
Contrastive Learning of Medical Visual Representations from Paired Images and Text
Computer Vision
Deep Learning to Assess Skeletal Maturity on Pediatric Hand Radiographs
Clinical Text Summarization
Optimizing the Factual Correctness of a Summary: A Study of Summarizing Radiology Reports
Terminology and Ontology
Expanding a radiology lexicon using contextual patterns in radiology reports
Research Policy
A Roadmap for Foundational Research on Artificial Intelligence in Medical Imaging: From the 2018 NIH/RSNA/ACR/The Academy Workshop
Opinion
Will AI Replace Radiologists?
Data Sharing Ethics
Ethics of Using and Sharing Clinical Imaging Data for AI
Radiology Reporting