通过频域分解与自适应状态空间模型，提升眼底OCT图像病灶检测精度。
RetiWave-Mamba: A Dual-Stream Network for Retinal Disease Detection based on Multi-scale Context and Frequency-Adaptive Mamba Projection
- 分频域处理：用小波变换分离高低频特征，分别建模结构与细节。
- 多尺度定位模块+注意力门控网络，精准定位病灶并抑制噪声传播。
- 自适应状态空间投影捕捉长程依赖，实现在噪声干扰下的98.25%准确率。
眼底疾病是导致不可逆视力损伤的主要原因，早期准确诊断对治疗至关重要。光学相干断层扫描（OCT）是关键成像手段，但其自动分析受固有斑点噪声、病灶尺度差异及类别间相似性影响。为此，我们提出新型框架RetiWave-Mamba，融合空间-频率域学习与前沿状态空间模型。该框架利用离散小波变换（DWT）将OCT图像分解为低频与高频流，实现结构上下文与细粒度特征的解耦处理。低频分支设计多尺度上下文定位模块（MCLM），结合多尺度空洞卷积与空间注意力，扩大全局感受野并精确定位病灶区域；高频分支引入注意力引导高分辨率网络（AG-HRNet），配备智能门控机制，抑制多尺度交互中的噪声传播。此外，引入频率自适应状态空间投影器（FAMP），捕获分离高频纹理特征中的长程依赖。在OCT-C8数据集上的大量实验表明，该方法达到98.25%的分类准确率，超越现有方法。结果验证了RetiWave-Mamba在噪声条件下稳健识别视网膜病变的有效性，为临床诊断提供有力工具。
原文摘要 · Abstract (English)
Retinal diseases are a leading cause of irreversible vision impairment, making early and accurate diagnosis essential for effective treatment. Optical Coherence Tomography (OCT) serves as a critical imaging modality for this purpose, yet its automated analysis is hindered by inherent speckle noise, varying lesion scales, and subtle inter-class similarities. To address these challenges, we propose a novel framework, RetiWave-Mamba, which integrates spatial-frequency domain learning with state-of-the-art state space models. The framework utilizes Discrete Wavelet Transform (DWT) to decompose OCT images into low- and high-frequency streams, enabling decoupled processing of structural context and fine-grained details. For the low-frequency branch, we design a Multi-scale Contextual Localization Module (MCLM), which synergizes multi-scale dilation with spatial attention to expand the global receptive field and precisely localize lesion regions. For the high-frequency branch, we introduce an Attention-Guided High-Resolution Network (AG-HRNet) equipped with an intelligent gating mechanism to suppress noise propagation during multi-scale interactions. Furthermore, a Frequency-Adaptive Mamba Projector (FAMP) is incorporated to capture long-range dependencies within disjoint high-frequency textural features. Extensive experiments on the OCT-C8 dataset demonstrate that our approach achieves a state-of-the-art (SOTA) classification accuracy of 98.25%, surpassing existing methods. These results highlight the efficacy of RetiWave-Mamba in robustly identifying retinal pathologies under noisy conditions, offering a promising tool for clinical diagnosis.
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