使用眼动追踪数据在胸部X光异常定位中对目标检测与短语定位模型的比较
A Comparison of Object Detection and Phrase Grounding Models in Chest X-ray Abnormality Localization using Eye-tracking Data
AI总结：
本文比较了目标检测与短语定位模型在胸部X光异常定位中的性能与可解释性，利用眼动追踪数据构建基线，证明文本能有效提升定位效果。
摘要：
胸部疾病是全球最普遍且最危险的健康问题之一。目标检测和短语定位深度学习模型解释复杂的放射学数据以辅助医疗专业人员进行诊断。目标检测定位类别的异常，而短语定位定位文本描述的异常。本文通过比较这两项任务的性能和可解释性，研究文本如何增强胸部X光中的异常定位。为建立可解释性基线，我们提出了一种自动流程，使用放射科医生的眼动追踪数据为报告句子生成图像区域。短语定位模型更好的性能（mIoU = 0.36 vs. 0.20）和可解释性（包含率 0.48 vs. 0.26）推断出文本在增强胸部X光异常定位中的有效性。
英文摘要：
Chest diseases rank among the most prevalent and dangerous global health issues. Object detection and phrase grounding deep learning models interpret complex radiology data to assist healthcare professionals in diagnosis. Object detection locates abnormalities for classes, while phrase grounding locates abnormalities for textual descriptions. This paper investigates how text enhances abnormality localization in chest X-rays by comparing the performance and explainability of these two tasks. To establish an explainability baseline, we proposed an automatic pipeline to generate image regions for report sentences using radiologists' eye-tracking data. The better performance - mIoU = 0.36 vs. 0.20 - and explainability - Containment ratio 0.48 vs. 0.26 - of the phrase grounding model infers the effectiveness of text in enhancing chest X-ray abnormality localization.
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。
