MOTIVATION: Radiology reports play a pivotal role in guiding treatment planning and enabling effective doctor-patient communication. However, their manual composition imposes a substantial workload on radiologists. Although automatic radiology report generation has emerged as a promising alternative, existing approaches predominantly rely on single-view chest X-rays and fail to adequately leverage patient-specific context, thereby limiting diagnostic accuracy. RESULTS: To address this challenge, we propose EVOKE, a novel chest X-ray report generation framework that incorporates multi-view contrastive learning and patient-specific knowledge. Specifically, we introduce a multi-view contrastive learning method that captures semantic correspondences both among multi-view radiographs within a study and between these radiographs and their associated report, thereby improving visual representation learning. We further present a knowledge-guided report generation module that integrates available patient-specific knowledge (i.e., indication, which includes symptom descriptions) to facilitate the generation of accurate and coherent radiology reports. To support research in multi-view report generation, we construct Multiview CXR and Two-view CXR datasets using publicly available sources. Our proposed EVOKE surpasses recent state-of-the-art methods across multiple datasets, achieving a 2.9% F1 RadGraph improvement on MIMIC-CXR, a 5.0% BLEU-1 improvement on MIMIC-ABN, a 1.5% BLEU-4 improvement on Multi-view CXR, and an 8.2% F1,mic-14 CheXbert improvement on Two-view CXR. AVAILABILITY: Code is publicly available at https://github.com/mk-runner/EVOKE, with an archived release available on Zenodo (doi:10.5281/zenodo.21000219).

Automated radiology report generation offers an effective solution to alleviate radiologists’ workload. However, most existing methods focus primarily on single or fixed-view images to model current disease conditions, which limits diagnostic accuracy and overlooks disease progression. Although some approaches utilize longitudinal data to track disease progression, they still rely on single images to analyze current visits. To address these issues, we propose enhanced contrastive learning with Multi-view Longitudinal data to facilitate chest X-ray Report Generation, named MLRG. Specifically, we introduce a multi-view longitudinal contrastive learning method that integrates spatial information from current multi-view images and temporal information from longitudinal data. This method also utilizes the inherent spatiotemporal information of radiology reports to supervise the pre-training of visual and textual representations. Subsequently, we present a tokenized absence encoding technique to flexibly handle missing patient-specific prior knowledge, allowing the model to produce more accurate radiology reports based on available prior knowledge. Extensive experiments on MIMIC-CXR, MIMIC-ABN, and Two-view CXR datasets demonstrate that our MLRG outperforms recent state-of-the-art methods, achieving a 2.3% BLEU-4 improvement on MIMIC-CXR, a 5.5% F1 score improvement on MIMIC-ABN, and a 2.7% F1 RadGraph improvement on Two-view CXR.