Langlotz Lab
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.
Main content start
RaVL: Discovering and Mitigating Spurious Correlations in Fine-Tuned Vision-Language Models
NeurIPS
Overview of the First Shared Task on Clinical Text Generation: RRG24 and" Discharge Me!"
ACL
A vision–language foundation model for the generation of realistic chest x-ray images
Nature BME
Radgraph-xl: A large-scale expert-annotated dataset for entity and relation extraction from radiology reports
Dataset
Merlin: A vision language foundation model for 3d computed tomography
Foundation Model
CheXpert Plus: Hundreds of Thousands of Aligned Radiology Texts, Images and Patients
Dataset
GREEN: Generative Radiology Report Evaluation and Error Notation
Evaluation Metric
Adapted large language models can outperform medical experts in clinical text summarization
Nature Medicine
CheXagent: Towards a Foundation Model for Chest X-Ray Interpretation
Foundation Models
ViLLA: Fine-Grained Vision-Language Representation Learning from Real-World Data
Self-Supervised Learning
RoentGen: Vision-Language Foundation Model for Chest X-ray Generation
Generative Models
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 Summarization
Improving the Factual Correctness of Radiology Report Generation with Semantic Rewards
Radiology Report Generation
Extracting Clinical Entities and Relations to Form Knowledge Graphs from Radiology Reports
Knowledge Graph Extraction
Contrastive Learning of Medical Visual Representations from Paired Images and Text
Contrastive Pre-Training
Deep Learning to Assess Skeletal Maturity on Pediatric Hand Radiographs
Computer Vision
Optimizing the Factual Correctness of a Summary: A Study of Summarizing Radiology Reports
Clinical Text Summarization
Expanding a radiology lexicon using contextual patterns in radiology reports
Terminology and Ontology
A Roadmap for Foundational Research on Artificial Intelligence in Medical Imaging: From the 2018 NIH/RSNA/ACR/The Academy Workshop
Research Policy
Will AI Replace Radiologists?
Opinion
Ethics of Using and Sharing Clinical Imaging Data for AI
Data Sharing Ethics
The Radiology Report: A Guide to Thoughtful Communication for Radiologists and Other Medical Professionals
Radiology Reporting