Seeing What Matters: Lesion-Aware High-Resolution Patch Discovery and Fusion for Chest X-ray Report Generation

Despite rapid advances in chest X-ray (CXR) foundationmodels, most radiology report generation (RRG) systems still rely onheavily downsampled inputs (e.g., 256×256) due to the fixed visual tokenbudgets of pretrained vision encoders, suppressing subtle yet clinicallyimportant cues present in native-resolution images. However, enablinghigh-resolution (high-res) perception remains challenging: naïve tilingcauses prohibitive token inflation, while global compression suppressessubtle lesions and degrades diagnostic fidelity. Inspired by radiologists’workflow, localizing suspicious regions before detailed high-res assess-ment. We propose Lesion-Aware High-Resolution Patch Discovery andFusion for Chest X-ray Reporting (LePaX), the first RRG frameworkthat enables efficient high-res CXR perception (up to 1920×1920) with-out increasing the vision-token count. LePaX formulates high-res percep-tion as a constrained spatial resolution allocation problem under a fixedtoken budget and introduces two key components: Learnable Spatial Res-olution Allocation (LSRA), which learns a spatial utility map that adap-tively allocates limited high-res capacity to diagnostically relevant re-gions, enabling targeted extraction of high-res patches from native CXRs;and Global–Regional Fusion (GRF), which performs token-preservingregion-to-global refinement by projecting high-resolution regional evi-dence back onto the global feature grid through spatially aligned resolu-tion write-back, avoiding token inflation. Experiments on multiple CXRbenchmarks demonstrate that LePaX consistently improves both clinicaland linguistic metrics while enabling native-resolution CXR perceptionwith over 10× fewer visual tokens than naïve high-res tiling.