CheXagent: Towards a Foundation Model for Chest X-Ray Interpretation
(2401.12208)Abstract
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 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}.
Overview
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The CheXagent project introduces an instruction-tuned foundation model (FM) for interpreting Chest X-Ray (CXR) images using the CheXinstruct dataset, which contains image-instruction-answer triplets.
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CheXagent combines a clinical Large Language Model (LLM) for understanding radiology reports, a vision encoder for CXR imagery, and a bridging network for vision-language integration.
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The team developed CheXbench, a systematic benchmark that assesses the FM's performance on eight clinically relevant CXR tasks, highlighting CheXagent's superior performance.
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Fairness evaluations were conducted to ensure the CheXagent model performs equitably across different demographic groups, identifying and aiming to correct performance disparities.
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The work done with CheXagent caters to the future of AI in medical imaging, making the training and evaluation tools available for public use and further research.
Introduction
The prospects for automated Chest X-Ray (CXR) interpretation have significantly advanced with the advent of vision-language foundation models (FMs). Despite this progress, CXR interpretation remains a challenging area due to three primary hurdles: the scarcity of large-scale vision-language medical datasets, the limitations of current vision and language encoders in medical contexts, and the lack of comprehensive evaluation frameworks for benchmarking CXR interpretation abilities of FMs.
Foundation Model for CXR Interpretation
The CheXagent project directly addresses these challenges. At its core, it presents an instruction-tuned FM for CXR interpretation, leveraging a foundation dataset named CheXinstruct. CheXinstruct consists of paired instruction-image-answer triplets from diverse sources, aimed at enhancing an FM's capacity to understand and interpret CXRs. CheXagent itself integrates three components: a clinical Large Language Model (LLM) trained for parsing radiology reports, a vision encoder to represent CXR images, and a bridging network uniting vision and language modalities.
CheXbench: Systematic Evaluation
With the development of CheXbench, the research team offers a systematic benchmark to evaluate an FM's efficacy across eight clinically-relevant CXR tasks. These tasks delve into aspects of image perception and textual understanding, assessed through tasks like view classification, disease identification, and visual question-answering. The thorough evaluation reveals CheXagent's superior performance over existing general-domain and medical-domain FMs, underscoring its robust capability in CXR interpretation tasks.
Fairness and Future Directions
An important aspect of model development, especially in healthcare, is ensuring equitable performance across demographic groups. The CheXagent team conducted an in-depth fairness evaluation considering factors such as sex, race, and age and highlighted areas where performance disparities exist. This objective analysis further informs enhancements to the model’s transparency and unbiased application.
In summary, the development and assessment of CheXagent encompass a significant leap toward a sophisticated FM for radiology. The integration of CheXinstruct and CheXbench allows for training and testing of a model that demonstrates substantial improvements in CXR interpretation, bolstered by rigorous analysis through expert radiologist evaluations and fairness assessments. With these tools and datasets now publicly available, the work paves the way for further advancements in AI-powered medical image interpretation.
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