  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 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 LLMs
alignment is employed to enhance the image-to-text transfer process, resulting
in sentences rich with clinical detail and improved explainability for
radiologist. Multi-center experiments validate both MRANet's 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. The
code is available at https://github.com/zzs95/MRANet.
