Interpretable Deeply Supervised Networks for Class-Imbalanced OCT Classification

Optical coherence tomography plays a critical role in diagnosing retinal diseases, yet automated deep learning classification is hindered by severe class imbalance in which rare pathologies are underrepresented and frequently misclassified, a limitation rarely exposed by the aggregate metrics reported in most prior work. We investigate a targeted intermediate-supervision framework, in which a secondary classifier head is attached to mid-level backbone features and jointly optimized with the primary classifier using inverse-frequency weighted loss. Unlike conventional deep supervision, which is primarily aimed at optimizing stability, the proposed formulation is used here to improve minority-class representation under severe OCT class imbalance. The method is evaluated on ResNet-18, ResNet-50, EfficientNet-B0, and ViT-B/16 using a four-class OCT dataset, with full per-class metrics reported across a systematic ablation of the auxiliary weight λ. EfficientNet-B0 achieved the best performance at λ = 0.3, attaining 97.78% accuracy, an AUROC of 0.995, and a Drusen F1-score of 93.51%, a gain of 2.64 percentage points over the unweighted baseline. Vision Transformers showed greater sensitivity to background padding artifacts than convolutional models. Grad-CAM and Attention Rollout analyses confirm that auxiliary supervision improves the localization of clinically relevant retinal structures, supporting its potential for interpretable, class-balanced automated OCT diagnosis.