VICCA：无需人类反馈的生成报告中胸部X光异常的视觉解释与理解
VICCA: Visual Interpretation and Comprehension of Chest X-ray Anomalies in Generated Report Without Human Feedback
AI总结：
提出VICCA多模态框架，结合Phrase Grounding Model与Text-to-Image Diffusion Module，通过双评分系统量化定位准确性与语义一致性，在病理定位和文本到图像对齐任务上取得SOTA，为无专家监督下验证医疗报告质量提供稳健机制。
摘要：
随着人工智能（AI）在医疗保健中变得日益核心，对可解释且值得信赖的模型的需求至关重要。当前的胸部X光（CXR）报告生成系统通常缺乏在无专家监督下验证输出的机制，引发了对可靠性和可解释性的担忧。为了应对这些挑战，我们提出了一种新型多模态框架，旨在增强AI生成的医疗报告的语义对齐和定位准确性。我们的框架集成了两个关键模块：一个Phrase Grounding Model，它基于文本提示识别并定位CXR图像中的病理；一个Text-to-Image Diffusion Module，它在保持解剖保真度的同时根据提示生成合成CXR图像。通过比较原始图像和生成图像之间的特征，我们引入了双评分系统：一个分数量化定位准确性，另一个评估语义一致性。该方法显著优于现有方法，在病理定位和文本到图像对齐方面取得了最先进的结果。短语定位与扩散模型的结合，加上双评分评估系统，为验证报告质量提供了稳健的机制，为医疗成像中更值得信赖和透明的AI铺平了道路。
英文摘要：
As artificial intelligence (AI) becomes increasingly central to healthcare, the demand for explainable and trustworthy models is paramount. Current report generation systems for chest X-rays (CXR) often lack mechanisms for validating outputs without expert oversight, raising concerns about reliability and interpretability. To address these challenges, we propose a novel multimodal framework designed to enhance the semantic alignment and localization accuracy of AI-generated medical reports. Our framework integrates two key modules: a Phrase Grounding Model, which identifies and localizes pathologies in CXR images based on textual prompts, and a Text-to-Image Diffusion Module, which generates synthetic CXR images from prompts while preserving anatomical fidelity. By comparing features between the original and generated images, we introduce a dual-scoring system: one score quantifies localization accuracy, while the other evaluates semantic consistency. This approach significantly outperforms existing methods, achieving state-of-the-art results in pathology localization and text-to-image alignment. The integration of phrase grounding with diffusion models, coupled with the dual-scoring evaluation system, provides a robust mechanism for validating report quality, paving the way for more trustworthy and transparent AI in medical imaging.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
