Papers by Christian Bluethgen
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. |
GREEN: Generative Radiology Report Evaluation and Error Notation (2024.findings-emnlp)
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Sophie Ostmeier, Justin Xu, Zhihong Chen, Maya Varma, Louis Blankemeier, Christian Bluethgen, Arne Md, Michael Moseley, Curtis Langlotz, Akshay Chaudhari, Jean-Benoit Delbrouck
| Challenge: | Existing automated evaluation metrics fail to consider factual correctness or are limited in their interpretability. |
| Approach: | They propose a radiology report evaluation metric that leverages natural language understanding of language models to identify and explain clinically significant errors. |
| Outcome: | The proposed method demonstrates higher correlation with expert error counts and higher alignment with expert preferences when compared to previous methods. |
Automated Structured Radiology Report Generation (2025.acl-long)
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Jean-Benoit Delbrouck, Justin Xu, Johannes Moll, Alois Thomas, Zhihong Chen, Sophie Ostmeier, Asfandyar Azhar, Kelvin Zhenghao Li, Andrew Johnston, Christian Bluethgen, Eduardo Pontes Reis, Mohamed S Muneer, Maya Varma, Curtis Langlotz
| Challenge: | Existing models struggle to produce consistent, clinically meaningful reports and standard evaluation metrics fail to capture the nuances of radiological interpretation. |
| Approach: | They propose to reformulate free-text radiology reports into a standardized format, ensuring clarity, consistency, and structured clinical reporting. |
| Outcome: | The proposed task reformulates free-text radiology reports into a standardized format, ensuring clarity, consistency, and structured clinical reporting. |
CheXalign: Preference fine-tuning in chest X-ray interpretation models without human feedback (2025.acl-long)
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Dennis Hein, Zhihong Chen, Sophie Ostmeier, Justin Xu, Maya Varma, Eduardo Pontes Reis, Arne Edward Michalson Md, Christian Bluethgen, Hyun Joo Shin, Curtis Langlotz, Akshay S Chaudhari
| Challenge: | Radiologists are a crucial role in translating medical images into actionable reports . however, the field faces staffing shortages and increasing workloads . |
| Approach: | They propose an automated pipeline for preference feedback focusing on chest X-ray radiology report generation (RRG) method leverages publicly available datasets containing pairs of images and radiologist-written reference reports with reference-based metrics, or Judges. |
| Outcome: | The proposed pipeline achieves state-of-the-art CheXbert scores on the MIMIC-CXR dataset while on average maintaining robust performance across six additional image perception and reasoning tasks. |