Automated radiology report generation aims to generate radiology reports that contain rich, fine-grained descriptions of radiology imaging. Compared with image captioning in the natural image domain, medical images are very similar to each other, with only minor differences in the occurrence of diseases. Given the importance of these minor differences in the radiology report, it is crucial to encourage the model to focus more on the subtle regions of disease occurrence. Secondly, the problem of visual and textual data biases is serious. Not only do normal cases make up the majority of the dataset, but sentences describing areas with pathological changes also constitute only a small part of the paragraph. Lastly, generating medical image reports involves the challenge of long text generation, which requires more expertise and empirical training in medical knowledge. As a result, the difficulty of generating such reports is increased. To address these challenges, we propose a disease-oriented retrieval framework that utilizes similar reports as prior knowledge references. We design a factual consistency captioning generator to generate more accurate and factually consistent disease descriptions. Our framework can find most similar reports for a given disease from the CXR database by retrieving a disease-oriented mask consisting of the position and morphological characteristics. By referencing the disease-oriented similar report and the visual features, the factual consistency model can generate a more accurate radiology report.

翻译：自动化放射学报告生成旨在生成包含丰富、细粒度描述的放射影像报告。与自然图像领域的图像描述相比，医学图像之间高度相似，仅在疾病发生部位存在微小差异。鉴于这些微小差异在放射学报告中的重要性，必须促使模型更多地关注疾病发生的细微区域。其次，视觉与文本数据偏差问题严重：不仅正常病例在数据集中占绝大多数，描述病理变化区域的句子也仅占段落的一小部分。最后，生成医学影像报告涉及长文本生成的挑战，这需要更多医学专业知识与经验训练，从而增加了报告生成的难度。为解决这些问题，我们提出一种面向疾病的检索框架，利用相似报告作为先验知识参考。我们设计了一个事实一致性标题生成器，以生成更准确且事实一致的疾病描述。该框架通过检索由位置与形态特征构成的疾病导向掩膜，能够从CXR数据库中为给定疾病找到最相似的报告。通过对疾病导向的相似报告与视觉特征进行参照，事实一致性模型可生成更精准的放射学报告。