To address these issues, we propose a novel Adaptive patch-word Matching (AdaMatch) model to correlate chest X-ray (CXR) image regions with words in medical reports and apply it to CXR-report generation to provide explainability for the generation process. AdaMatch exploits the fine-grained relation between adaptive patches and words to provide explanations of specific image regions with corresponding words. To capture the abnormal regions of varying sizes and positions, we introduce the Adaptive Patch extraction (AdaPatch) module to acquire the adaptive patches for these regions adaptively. In order to provide explicit explainability for CXR-report generation task, we propose an AdaMatch-based bidirectional large language model for Cyclic CXR-report generation (AdaMatch-Cyclic). It employs the AdaMatch to obtain the keywords for CXR images and `keypatches' for medical reports as hints to guide CXR-report generation. Extensive experiments on two publicly available CXR datasets prove the effectiveness of our method and its superior performance to existing methods.

通过引入自适应 Patch-Word 匹配模型，我们提出了一种用于相关联胸部 X 光（CXR）图像区域与医学报告中的单词并应用于 CXR 报告生成以解释生成过程的新型 AdaMatch 模型。AdaMatch 利用自适应 patch 和单词之间的细粒度关系，为指定图像区域提供相应的单词解释。通过引入自适应 Patch 提取模块，我们可以捕捉变化大小和位置的异常区域的自适应 patch。为了为 CXR 报告生成任务提供明确的解释性，我们提出了一种基于 AdaMatch 的循环 CXR 报告生成模型（AdaMatch-Cyclic）。它利用 AdaMatch 来获取 CXR 图像的关键字，并将医学报告的关键 patch 作为指导 CXR 报告生成的提示。对两个公开可用的 CXR 数据集的大量实验证明了我们方法的有效性及其优越的性能。

医学影像中的细粒度图像-文本对齐实现循环图像-报告生成