Beyond Accuracy: Quantifying Pulmonary Attribution in Anatomy-Guided Chest X-Ray Classification Under Domain Shift

arXiv:2608.30467v1 Announce Type: new Abstract: Deep-learning models can achieve strong chest X-ray (CXR) classification performance without establishing whether their predictions predominantly rely on pulmonary image content. This study evaluates pulmonary attribution containment as an anatomy-related reliability property distinct from diagnostic performance. We propose DBCA-SegNet-MGAP, a multi-task anatomy-guided CNN-Transformer framework that combines complementary feature representations through bidirectional cross-backbone attention, predicts a soft lung mask, and incorporates this anatomical prior directly into classification through Mask-Guided Adaptive Global Average Pooling (MGAP). Pulmonary attribution containment is quantified using the Anatomical Local Energy Ratio (ALR) and high-intensity cumulative ALR (cALR@0.9). Experiments were repeated across three training seeds using the COVID-19 Radiography Database for four-class internal testing and a locked Shenzhen-to-Montgomery protocol for zero-shot external tuberculosis testing. On COVID-19, the proposed model achieved a weighted F1 of 0.9615 pm 0.0015 and macro ROC-AUC of 0.9906 pm 0.0007. In an architecture-matched dual-bridge comparison, replacing conventional GAP with MGAP increased ALR from 0.3878 pm 0.0098 to 0.7086 pm 0.0104 and cALR@0.9 from 0.5265 pm 0.0101 to 0.9905 pm 0.0018, while weighted F1 remained essentially unchanged (0.9618 pm 0.0015 vs. 0.9615 pm 0.0015). Under locked external transfer to Montgomery, ROC-AUC remained 0.9080 pm 0.0043 and pulmonary ALR remained 0.6466 pm 0.0081, whereas weighted F1 decreased to 0.7528 pm 0.0080 and ECE increased to 0.1683 pm 0.0055. These findings show that diagnostic discrimination, calibration, and pulmonary attribution containment are distinct model properties and support their joint evaluation under internal testing and external domain shift.

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Source: arXiv cs.CV | 2026-09-01