AGIRA: Anatomy-Guided Image–Report Alignment with Finite-Scale Fractal Analysis for Chest X-Ray Representation Learning

Background/Objectives: Chest radiographs contain hierarchical relationships between global thoracic configuration and organ-specific findings, together with scale-dependent anatomical boundary geometry and heterogeneous texture distributions. Coarse image–report alignment may overlook regional evidence, whereas isolated local alignment can fragment complete-examination semantics. We propose Anatomy-Guided Image–Report Alignment (AGIRA) for fine-grained chest X-ray (CXR) representation learning and investigate its behavior through post hoc finite-scale fractal and multifractal analysis. Methods: AGIRA constructs whole-image, left-lung, right-lung, and heart image–text pairs, adapts frozen vision and language backbones using lightweight Anatomy Sensors, and retains masked image and language reconstruction objectives to preserve global–local semantic continuity. Temporal–multiview fusion and hybrid soft labels reduce false-negative supervision. Post hoc analysis estimates effective box-counting dimensions, generalized dimensions, multifractal spectrum width, and a finite-scale attention concentration index. We additionally compare these descriptors with conventional intensity, texture, and uncertainty features using nested cross-validated incremental-value analyses. Results: AGIRA achieves 92.0% AUC, 84.2% accuracy, and 79.0% F1-score on RSNA Pneumonia, together with 57.5% zero-shot accuracy and 53.7% image-to-text P@5 on CheXpert 5 × 200, while updating only 7.54% of the complete model. On an independently annotated 600-unit report-routing set, the deterministic parser obtains macro-F1 0.953 and exact multi-label routing accuracy 0.918. Under the identical 34,080-record cohort, AGIRA remains above GLoRIA-ViT, MLIP, and BCC on all reported downstream metrics. Adding finite-scale descriptors to conventional intensity/texture/uncertainty features increases cross-validated AUROC for identifying anatomy-alignment-sensitive cases from 0.671 to 0.731 for zero-shot classification and from 0.651 to 0.704 for retrieval. Mild anatomical crop perturbations (±5%) change CheXpert P@5 and zero-shot accuracy by less than one percentage point, whereas severe 10% perturbations and explicit anatomy swaps cause progressively larger degradation. Conclusions: AGIRA improves cross-dataset benchmark transfer and retrieval while preserving parameter efficiency. The post hoc fractal/multifractal analysis contributes measurable incremental explanatory information about anatomy-alignment-sensitive model behavior beyond conventional descriptors without entering model training, and the added controlled experiments support parser reliability, cohort fairness, parameter robustness, and tolerance to moderate anatomical-localization noise.