تقرير
CheXagent: Towards a Foundation Model for Chest X-Ray Interpretation
| العنوان: | CheXagent: Towards a Foundation Model for Chest X-Ray Interpretation |
|---|---|
| المؤلفون: | Chen, Zhihong, Varma, Maya, Delbrouck, Jean-Benoit, Paschali, Magdalini, Blankemeier, Louis, Van Veen, Dave, Valanarasu, Jeya Maria Jose, Youssef, Alaa, Cohen, Joseph Paul, Reis, Eduardo Pontes, Tsai, Emily B., Johnston, Andrew, Olsen, Cameron, Abraham, Tanishq Mathew, Gatidis, Sergios, Chaudhari, Akshay S., Langlotz, Curtis |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Computer Vision and Pattern Recognition, Computer Science - Computation and Language |
| الوصف: | Chest X-rays (CXRs) are the most frequently performed imaging test in clinical practice. Recent advances in the development of vision-language foundation models (FMs) give rise to the possibility of performing automated CXR interpretation, which can assist physicians with clinical decision-making and improve patient outcomes. However, developing FMs that can accurately interpret CXRs is challenging due to the (1) limited availability of large-scale vision-language datasets in the medical image domain, (2) lack of vision and language encoders that can capture the complexities of medical data, and (3) absence of evaluation frameworks for benchmarking the abilities of FMs on CXR interpretation. In this work, we address these challenges by first introducing \emph{CheXinstruct} - a large-scale instruction-tuning dataset curated from 28 publicly-available datasets. We then present \emph{CheXagent} - an instruction-tuned FM capable of analyzing and summarizing CXRs. To build CheXagent, we design a clinical large language model (LLM) for parsing radiology reports, a vision encoder for representing CXR images, and a network to bridge the vision and language modalities. Finally, we introduce \emph{CheXbench} - a novel benchmark designed to systematically evaluate FMs across 8 clinically-relevant CXR interpretation tasks. Extensive quantitative evaluations and qualitative reviews with five expert radiologists demonstrate that CheXagent outperforms previously-developed general- and medical-domain FMs on CheXbench tasks. Furthermore, in an effort to improve model transparency, we perform a fairness evaluation across factors of sex, race and age to highlight potential performance disparities. Our project is at \url{https://stanford-aimi.github.io/chexagent.html}. Comment: 24 pages, 8 figures |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2401.12208 |
| رقم الأكسشن: | edsarx.2401.12208 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |