Noise-aware dynamic convolution for improved generalizability of retinal disease diagnosis using optical coherence tomography images.

Significance: Optical coherence tomography (OCT) is widely used for the diagnosis of retinal diseases. However, deep learning models trained on a single dataset often degrade when deployed across scanners and clinical sites due to device-dependent speckle variability and acquisition differences, limiting their reliability in real-world screening. Aim: We aim to develop a lightweight deep learning framework that leverages speckle characteristics in OCT images to improve cross-scanner generalizability for retinal disease classification while preserving real-time inference efficiency. Approach: We propose NA-DyCNN, a noise-aware dynamic convolutional neural network that minimizes the expected classification risk over multiple stochastic realizations of multiplicative speckle perturbations and regularizes the dynamic routing mechanism to produce scanner-invariant kernel mixtures. The framework was evaluated using over 105,000 B-scans from three heterogeneous OCT cohorts under strict zero-shot cross-dataset transfer. Results: NA-DyCNN consistently outperformed lightweight baselines across four zero-shot cross-dataset transfer scenarios, achieving up to 92.87% accuracy, a weighted Conclusions: Modeling postacquisition speckle variability during training improves the generalizability of OCT classifiers without increasing the inference cost, thereby enabling the more reliable deployment of artificial intelligence (AI)-assisted retinal screening across heterogeneous imaging systems.