Multi-Modality Regional Alignment Network for Covid X-Ray Survival Prediction and Report Generation

作者：Zhusi Zhong, Jie Li, John Sollee, Scott Collins, Harrison X. Bai, Paul J. Zhang, Terrence Healey, Michael K. Atalay, Xinbo Gao, Zhicheng Jiao · 发表于：IEEE Journal of Biomedical and Health Informatics · 年份：2024 · DOI：10.1109/jbhi.2024.3417849 · 被引用次数：7 · 研究领域：COVID-19 diagnosis using AI、Radiomics and Machine Learning in Medical Imaging

In response to the worldwide COVID-19 pandemic, advanced automated technologies have emerged as valuable tools to aid healthcare professionals in managing an increased workload by improving radiology report generation and prognostic analysis. This study proposes a Multi-modality Regional Alignment Network (MRANet), an explainable model for radiology report generation and survival prediction that focuses on high-risk regions. By learning spatial correlation in the detector, MRANet visually grounds region-specific descriptions, providing robust anatomical regions with a completion strategy. The visual features of each region are embedded using a novel survival attention mechanism, offering spatially and risk-aware features for sentence encoding while maintaining global coherence across tasks. A cross-domain LLMs-Alignment is employed to enhance the image-to-text transfer process, resulting in sentences rich with clinical detail and improved explainability for radiologists. Multi-center experiments validate the overall performance and each module's composition within the model, encouraging further advancements in radiology report generation research emphasizing clinical interpretation and trustworthiness in AI models applied to medical studies.