Crossdr: Bridging 2D And 3D Features For Diabetic Retinopathy Classification Using Context-Aware Cross-Attention

This paper introduces CrossDR, a novel approach unifying 2D and 3D feature representations through a context-aware cross-attention mechanism for diabetic retinopathy (DR) classification in 3D Optical Coherence Tomography (OCT) scans. The model uncovers critical diagnostic cues embedded in 3D-OCT images, which offer rich information for DR detection. The framework comprises three core components: the Lightweight Attention (LA) block, a CNN-based encoder, and the Context-Aware Cross-Attention (CA)<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> block. The LA block highlights volume-specific features that are crucial for DR diagnosis. It achieves this by applying a series of 1×1 convolutions followed by softmax operations, enabling the extraction of spatially informative DR-specific features. Meanwhile, the CNN-based encoder extracts high-level semantic features from the 3D-OCT slices, offering a robust representation of retinal structures. The (CA)<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> block further boosts the model’s performance by dynamically capturing and reweighting feature dependencies. This block models relationships between feature maps at different abstraction levels, improving the discriminative power of the extracted features. By integrating these refined features through the (CA)<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> block, the model achieves accurate DR classification. The CrossDR framework was evaluated on 481 volumetric OCT images, outperforming existing state-of-the-art methods with an accuracy of 94%. Multiple experiments and ablation studies were conducted to assess our model’s performance.