Search Results for author: Christian Bluethgen

Found 11 papers, 5 papers with code

GREEN: Generative Radiology Report Evaluation and Error Notation

no code implementations6 May 2024 Sophie Ostmeier, Justin Xu, Zhihong Chen, Maya Varma, Louis Blankemeier, Christian Bluethgen, Arne Edward Michalson, Michael Moseley, Curtis Langlotz, Akshay S Chaudhari, Jean-Benoit Delbrouck

Evaluating radiology reports is a challenging problem as factual correctness is extremely important due to the need for accurate medical communication about medical images.

Natural Language Understanding

A Benchmark of Domain-Adapted Large Language Models for Generating Brief Hospital Course Summaries

no code implementations8 Mar 2024 Asad Aali, Dave Van Veen, Yamin Ishraq Arefeen, Jason Hom, Christian Bluethgen, Eduardo Pontes Reis, Sergios Gatidis, Namuun Clifford, Joseph Daws, Arash S. Tehrani, Jangwon Kim, Akshay S. Chaudhari

To enable the adaptation of LLMs for BHC synthesis, we introduce a novel benchmark consisting of a pre-processed dataset extracted from MIMIC-IV notes, encapsulating clinical note, and brief hospital course (BHC) pairs.

In-Context Learning

RoentGen: Vision-Language Foundation Model for Chest X-ray Generation

1 code implementation23 Nov 2022 Pierre Chambon, Christian Bluethgen, Jean-Benoit Delbrouck, Rogier van der Sluijs, Małgorzata Połacin, Juan Manuel Zambrano Chaves, Tanishq Mathew Abraham, Shivanshu Purohit, Curtis P. Langlotz, Akshay Chaudhari

We present evidence that the resulting model (RoentGen) is able to create visually convincing, diverse synthetic CXR images, and that the output can be controlled to a new extent by using free-form text prompts including radiology-specific language.

Data Augmentation

Improving the Factual Correctness of Radiology Report Generation with Semantic Rewards

1 code implementation21 Oct 2022 Jean-Benoit Delbrouck, Pierre Chambon, Christian Bluethgen, Emily Tsai, Omar Almusa, Curtis P. Langlotz

To overcome this limitation, we propose a new method, the RadGraph reward, to further improve the factual completeness and correctness of generated radiology reports.

named-entity-recognition Named Entity Recognition +1

Adapting Pretrained Vision-Language Foundational Models to Medical Imaging Domains

no code implementations9 Oct 2022 Pierre Chambon, Christian Bluethgen, Curtis P. Langlotz, Akshay Chaudhari

Multi-modal foundation models are typically trained on millions of pairs of natural images and text captions, frequently obtained through web-crawling approaches.

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