Advancing OCT-Based Retinal Disease Classification with XLSTM: A Framework for Variable-Length Volume Processing

This paper presents a method for retinal disease classification using optical coherence tomography (OCT) scans, specifically addressing the challenge of variable B-scan density across dataset volumes. Deep learning methods often struggle with fluctuations in the number of B-scans within OCT volumes, which can limit their effectiveness. To address this, we propose an extended long short-term memory (xLSTM) model capable of processing variable-length B-scan sequences, capturing spatial and contextual information across entire volumes regardless of slice count. Leveraging xLSTM's advanced memory structure, our method enhances classification accuracy while maintaining computational efficiency. We evaluated the xLSTM-based approach on public and private datasets, demonstrating its robustness against inter-study variability and domain shifts. Experimental results show that this approach performs on par with or surpasses state-of-the-art baselines and offers a scalable solution for accurate 3D volume classification.