Glance Before You Tell: Anomaly-Guided 3D Radiology Report Generation with Heat-Conduction Slice Encoders

Generating accurate radiology reports from 3D medical volumes remains prone to hallucination. Existing grounding strategies often depend on external visual foundation models or on fixed medical phrase queues. The former passes segmenter-derived evidence to generation, where segmentation errors are hard to suppress. The latter has limited coverage of long-tail and compositional abnormalities. We therefore propose HeatRAD, a glance-before-tell framework that first routes suspicious slice evidence into an organ-ordered visual prefix, then generates reports from this localized evidence without explicit grounding supervision. HeatRAD uses a slice-centric signed heat-conduction encoder that performs efficient spectral mixing while separating smooth anatomy from lesion-like residues. \method{} then scores suspicious slices, binds their evidence to coarse organ contexts, and assembles an organ-ordered visual prefix, guiding the decoder toward localized abnormalities instead of redundant volumetric context. Experiments on three 3D radiology benchmarks show consistent gains in clinical fidelity and hallucination-related metrics, supporting anomaly-first evidence selection as an annotation-efficient paradigm for 3D report generation.