Pierre Chambon — accepted AI-conference papers

2024

RadGraph-XL: A Large-Scale Expert-Annotated Dataset for Entity and Relation Extraction from Radiology Reports

Jean-Benoit Delbrouck, Pierre Chambon, Zhihong Chen, Maya Varma, Andrew Johnston, Louis Blankemeier +4

ACL 2024findings

In order to enable extraction of structured clinical data from unstructured radiology reports, we introduce RadGraph-XL, a large-scale, expert-annotated dataset for clinical entity and relation extraction. RadGraph-XL consists of 2,300 radiology reports, which are annotated with over 410,000 entitie…

Cited by 7 Source ↗PDF ↗code ↗Scholar ↗

2022

Improving the Factual Correctness of Radiology Report Generation with Semantic Rewards

Jean-Benoit Delbrouck, Pierre Chambon, Christian Bluethgen, Emily Tsai, Omar Almusa, Curtis Langlotz

EMNLP 2022finding

Neural image-to-text radiology report generation systems offer the potential to improve radiology reporting by reducing the repetitive process of report drafting and identifying possible medical errors. These systems have achieved promising performance as measured by widely used NLG metrics such as…

Cited by 73 Source ↗PDF ↗code ↗Scholar ↗

2021

RadGraph: Extracting Clinical Entities and Relations from Radiology Reports

Saahil Jain, Ashwin Agrawal, Adriel Saporta, Steven Truong, Du Nguyen Duong, Tan Bui +6

NeurIPS 2021poster

Extracting structured clinical information from free-text radiology reports can enable the use of radiology report information for a variety of critical healthcare applications. In our work, we present RadGraph, a dataset of entities and relations in full-text chest X-ray radiology reports based on…

Cited by 229 Source ↗Scholar ↗