HMA-DER: A Hierarchical Attention and Expert Routing Framework for Accurate Gastrointestinal Disease Diagnosis

Deep learning is employed increasingly in Gastroenterology (GI) endoscopy computer-aided diagnostics for polyp segmentation and multi-class disease detection. In the real world, implementation requires high accuracy, therapeutically relevant explanations, strong calibration, domain generalization, and efficiency. Current Convolutional Neural Network (CNN) and transformer models compromise border precision and global context, generate attention maps that fail to align with expert reasoning, deteriorate during cross-center changes, and exhibit inadequate calibration, hence diminishing clinical trust.

HMA-DER is a hierarchical multi-attention architecture that uses dilation-enhanced residual blocks and an explainability-aware Cognitive Alignment Score (CAS) regularizer to directly align attribution maps with reasoning signals from experts. The framework has additions that make it more resilient and a way to test for accuracy, macro-averaged F1 score, Area Under the Receiver Operating Characteristic Curve (AUROC), calibration (Expected Calibration Error (ECE), Brier Score), explainability (CAS, insertion/deletion AUC), cross-dataset transfer, and throughput.

HMA-DER gets Dice Similarity Coefficient scores of 89.5% and 86.0% on Kvasir-SEG and CVC-ClinicDB, beating the strongest baseline by +1.9 and +1.7 points. It gets 86.4% and 85.3% macro-F1 and 94.0% and 93.4% AUROC on HyperKvasir and GastroVision, which is better than the baseline by +1.4/+1.6 macro-F1 and +1.2/+1.1 AUROC. Ablation study shows that hierarchical attention gives the highest (+3.0), followed by CAS regularization (+2–3), dilatation (+1.5–2.0), and residual connections (+2–3). Cross-dataset validation demonstrates competitive zero-shot transfer (e.g., KS $\to$CVC Dice 82.7%), whereas multi-dataset training diminishes the domain gap, yielding an 88.1% primary-metric average. HMA-DER’s mixed-precision inference can handle 155 pictures per second, which helps with calibration.

HMA-DER strikes a compromise between accuracy, explainability, robustness, and efficiency for the use of reliable GI computer-aided diagnosis in real-world clinical settings.