SOURCE-LINKED INTELLIGENCE

SGRNet: Spatially Guided Radiology Network for Structured Radiological Reporting of Head and Neck Cancer

arXiv · AI, language, vision and robotics · article · Aug 29, 2026 · UTC

Automated radiological report generation can alleviate clinical workloads and eliminate observer variability. However, standard free-text generation models pose hallucination risks in dense regions and fail under data scarcity. We address these challenges in Head and Neck Cancer (HNC) from contrast-enhanced CT (CECT) imaging. To enforce factual safety, we reformulate report generation as an anatomically grounded, multi-label, structured reporting task, predicting localized tumor involvement across a hierarchical clinical schema. To bridge the visual gap from missing metabolic imaging (e.g., PE

Read original source ↗ Open in workspace

recordType

paper

region

Global