Logic-Driven Concept Bottleneck for Interpretable Radiology Report Generation

Automated radiology report generation helps alleviate radiologists' workload and enhance diagnostic accuracy. However, current deep learning-based radiology report generation models often lack interpretability and logical consistency. The semantically agnostic nature of their reasoning processes often fails to align visual findings with clinical diagnoses, leading to factual inconsistencies. To address these challenges, we propose LoCoGen, a novel model for radiology report generation, which leverages a logic-driven concept bottleneck to perform diagnostic inference without relying on ground-truth annotations for the intermediate clinical concepts. Specifically, LoCoGen learns to predict intermediate concepts by optimizing high-level disease classification through logic-driven formulas linking concepts and diseases. The resulting inference paths are then integrated with clinical context and visual features as prompts to guide a pre-trained language model in generating accurate and interpretable reports. Extensive experiments on the MIMIC-CXR dataset demonstrate that our model achieves superior performance compared to state-of-the-art models. Qualitative analysis further validates the interpretability and diagnostic accuracy of LoCoGen. Code is available at https://anonymous.4open.science/r/LoCoGen-zj8snwin2j0d/.