Report generation models offer fine-grained textual interpretations of medical images like chest X-rays, yet they often lack interactivity (i.e. the ability to steer the generation process through user queries) and localized interpretability (i.e. visually grounding their predictions), which we deem essential for future adoption in clinical practice. While there have been efforts to tackle these issues, they are either limited in their interactivity by not supporting textual queries or fail to also offer localized interpretability. Therefore, we propose a novel multitask architecture and training paradigm integrating textual prompts and bounding boxes for diverse aspects like anatomical regions and pathologies. We call this approach the Chest X-Ray Explainer (ChEX). Evaluations across a heterogeneous set of 9 chest X-ray tasks, including localized image interpretation and report generation, showcase its competitiveness with SOTA models while additional analysis demonstrates ChEX's interactive capabilities.

通过结合文本提示和边界框，我们提出了一种新颖的多任务架构和训练方法，用于解决医学图像的报告生成和解释性问题。我们称之为胸部 X 射线解释器（ChEX）。通过在9个不同的胸部X射线任务中进行评估，包括定位图像解释和报告生成，我们展示了ChEX与SOTA模型的竞争力，并进一步分析证明了ChEX的交互能力。

ChEX：胸部X光片的交互式定位和区域描述