DAVis-Net: A Dual-Attention Deep Supervision Framework for Reliable Retinal OCT Image Classification with Integrated Explainability and Uncertainty Quantification

Abstract

Classification accuracy in retinal optical coherence tomography (OCT) alone does not establish whether a model is reliable or whether to refer to a specialist. To address this, we propose DAVis-Net, a VGG16-based architecture that is equipped with two Convolutional Block Attention Modules (CBAMs), an auxiliary deep-supervision head, and an integrated reliability framework that includes Monte Carlo Dropout uncertainty estimation, model calibration, split conformal prediction, and quantitative multi-method attribution analysis. DAVis-Net has achieved a cross-validated accuracy of 98.02% ± 0.12% on the four classes of OCT (CNV, DME, DRUSEN, NORMAL), statistically significantly higher than the VGG16 baseline in a matched-fold paired comparison (accuracy: p = 0.023; macro-F1: p = 0.004), with the highest gain on the hardest class (DRUSEN F1 +4.57 pp). Joint correlation analysis showed strong redundancy between predictive entropy and conformal set size (r = 0.67–0.78) across all classes, and a near zero linear correlation between both of these and a geometric proxy for spatial attention placement (|r| < 0.08), suggesting that, as captured by this central-region localization proxy, distributional uncertainty and spatial attention placement may reflect largely distinct reliability dimensions. A composite Trust/Refer triage rule achieved an accuracy of 99.76% on the 73.9% of cases it retained. All reported figures are internal estimates derived from a single dataset at the image-level, and multi-center validation is still required. The results show that the multi-dimensional reliability assessment is a more informative characterization of a medical image classifier than accuracy alone.