SGSeg：通过自引导实现胸部X光片语言引导分割中的无文本推理
SGSeg: Enabling Text-free Inference in Language-guided Segmentation of Chest X-rays via Self-guidance
AI总结：
本文提出SGSeg自引导分割框架，通过定位增强报告生成模块在训练时利用语言引导、推理时无需文本，首次实现胸部X光片语言引导分割中的无文本推理，并在QaTa-COV19数据集上超越现有单模态方法、接近多模态方法性能。
摘要：
胸部X光片中感染区域的分割对于准确描绘肺部结构和病理异常至关重要。近年来，多模态语言引导的图像分割方法已成为胸部X光片的一种有前景的解决方案，其中描述图像评估的临床文本报告被用作引导。然而，现有的语言引导方法需要临床报告与图像同时存在，因此它们不适用于决策支持场景下的图像分割，而仅限于临床报告完成后的回顾性图像分析。在本研究中，我们提出了一种自引导分割框架（SGSeg），该框架在训练阶段利用语言引导（多模态），同时支持无文本推理（单模态），这是语言引导分割中首个实现无文本推理的方法。我们利用文本报告中描绘的肺部结构和病理结构的关键位置信息，并引入了一种新颖的定位增强报告生成（LERG）模块，用于生成自引导所需的临床报告。我们的LERG集成了一个目标检测器和一个基于位置的注意力聚合器，并通过位置感知伪标签提取模块进行弱监督。在广泛使用的QaTa-COV19基准数据集上的大量实验表明，我们的SGSeg取得了优于现有单模态分割方法的性能，并紧密接近多模态语言引导分割方法的最新性能。
英文摘要：
Segmentation of infected areas in chest X-rays is pivotal for facilitating the accurate delineation of pulmonary structures and pathological anomalies. Recently, multi-modal language-guided image segmentation methods have emerged as a promising solution for chest X-rays where the clinical text reports, depicting the assessment of the images, are used as guidance. Nevertheless, existing language-guided methods require clinical reports alongside the images, and hence, they are not applicable for use in image segmentation in a decision support context, but rather limited to retrospective image analysis after clinical reporting has been completed. In this study, we propose a self-guided segmentation framework (SGSeg) that leverages language guidance for training (multi-modal) while enabling text-free inference (uni-modal), which is the first that enables text-free inference in language-guided segmentation. We exploit the critical location information of both pulmonary and pathological structures depicted in the text reports and introduce a novel localization-enhanced report generation (LERG) module to generate clinical reports for self-guidance. Our LERG integrates an object detector and a location-based attention aggregator, weakly-supervised by a location-aware pseudo-label extraction module. Extensive experiments on a well-benchmarked QaTa-COV19 dataset demonstrate that our SGSeg achieved superior performance than existing uni-modal segmentation methods and closely matched the state-of-the-art performance of multi-modal language-guided segmentation methods.
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。
