Papers by Pierre Chambon
Improving the Factual Correctness of Radiology Report Generation with Semantic Rewards (2022.findings-emnlp)
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Jean-Benoit Delbrouck, Pierre Chambon, Christian Bluethgen, Emily Tsai, Omar Almusa, Curtis Langlotz
| Challenge: | Neural image-to-text radiology report generation systems have been successful on NLG metrics, but they are not factually complete or consistent due to inadequate training and evaluation. |
| Approach: | They propose a method to improve the factual completeness and correctness of generated radiology reports by using a dataset containing annotated chest X-ray images. |
| Outcome: | The proposed method significantly improves factual completeness and correctness of generated radiology reports on two open radiology report datasets. |
RadGraph-XL: A Large-Scale Expert-Annotated Dataset for Entity and Relation Extraction from Radiology Reports (2024.findings-acl)
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Jean-Benoit Delbrouck, Pierre Chambon, Zhihong Chen, Maya Varma, Andrew Johnston, Louis Blankemeier, Dave Van Veen, Tan Bui, Steven Truong, Curtis Langlotz
| Challenge: | RadGraph-XL is an expert-annotated dataset for structured clinical data extraction. |
| Approach: | They propose a large-scale, expert-annotated dataset for clinical entity and relation extraction using radiology reports. |
| Outcome: | The proposed model outperforms existing methods by up to 52% and outperfies GPT-4 in this domain. |
ViLMedic: a framework for research at the intersection of vision and language in medical AI (2022.acl-demo)
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Jean-benoit Delbrouck, Khaled Saab, Maya Varma, Sabri Eyuboglu, Pierre Chambon, Jared Dunnmon, Juan Zambrano, Akshay Chaudhari, Curtis Langlotz
| Challenge: | Multimodal medical AI is a growing field of interest, especially for tasks that involve multimodal data. |
| Approach: | They propose a vision-and-language medical library to improve multimodal medical predictions and enable new applications. |
| Outcome: | The vision-and-language medical library aims to improve reproducibility and speed up progress across medical AI . it contains a dozen implementations replicating state-of-the-art results on medical datasets . the library is extensible by researchers but also simple for practitioners . |