Anatomical abnormality detection and report generation of chest X-ray (CXR) are two essential tasks in clinical practice. The former aims at localizing and characterizing cardiopulmonary radiological findings in CXRs, while the latter summarizes the findings in a detailed report for further diagnosis and treatment. Existing methods often focused on either task separately, ignoring their correlation. This work proposes a co-evolutionary abnormality detection and report generation (CoE-DG) framework. The framework utilizes both fully labeled (with bounding box annotations and clinical reports) and weakly labeled (with reports only) data to achieve mutual promotion between the abnormality detection and report generation tasks. Specifically, we introduce a bi-directional information interaction strategy with generator-guided information propagation (GIP) and detector-guided information propagation (DIP). For semi-supervised abnormality detection, GIP takes the informative feature extracted by the generator as an auxiliary input to the detector and uses the generator's prediction to refine the detector's pseudo labels. We further propose an intra-image-modal self-adaptive non-maximum suppression module (SA-NMS). This module dynamically rectifies pseudo detection labels generated by the teacher detection model with high-confidence predictions by the student.Inversely, for report generation, DIP takes the abnormalities' categories and locations predicted by the detector as input and guidance for the generator to improve the generated reports.

翻译：胸部X光（CXR）的解剖异常检测与报告生成是临床实践中的两项核心任务。前者旨在定位并表征CXR中的心肺放射学征象，后者则将发现汇总为详细报告以供进一步诊断与治疗。现有方法往往独立处理这两项任务，忽视了其内在关联性。本研究提出一种协同演化异常检测与报告生成（CoE-DG）框架，该框架通过联合利用全标注数据（含边界框标注与临床报告）和弱标注数据（仅含报告），实现异常检测与报告生成任务的相互促进。具体而言，我们设计了包含生成器引导信息传播（GIP）与检测器引导信息传播（DIP）的双向信息交互策略。针对半监督异常检测任务，GIP将生成器提取的信息特征作为检测器的辅助输入，并利用生成器的预测优化检测器的伪标签。我们进一步提出图像模态内自适应非极大值抑制模块（SA-NMS），该模块通过学生模型的高置信度预测动态修正教师检测模型生成的伪检测标签。相反地，对于报告生成任务，DIP以检测器预测的异常类别与位置作为输入和引导信息，提升生成报告的质量。