Energy-Based Controllable Radiology Report Generation with Medical Knowledge

Automated generation of radiology reports from chest X-rays has the potential to substantially reduce the workload of radiologists. Recent advances in report generation using deep learning algorithms have achieved significant results, benefiting from the incorporation of medical knowledge. However, incorporation of additional knowledge or constraints in existing models often require either altering network structures or task-specific fine-tuning. In this paper, we propose an energy-based controllable report generation method, named ECRG. Specifically, our method directly utilizes diverse off-the-shelf medical expert models or knowledge to design energy functions, which are integrated into pre-trained report generation models during the inference stage, without any alterations to the network structure or fine-tuning. We also propose an acceleration algorithm to improve the efficiency of sampling the complex multi-modal distribution of report generation. ECRG is model-agnostic and can be readily used for other pre-trained report generation models. Two cases are presented on the design of energy functions tailored to medical expert systems and knowledge. The experiments on widely used datasets Chest ImaGenome v1.0.0 and MIMIC-CXR demonstrate the effectiveness of our proposed approach.