融合高效卷积与状态空间模型，提升乳腺影像良恶性分类准确率
A Hybrid Architecture for Benign-Malignant Classification of Mammography ROIs
- 用EfficientNetV2-M提取局部特征，Vision Mamba建模全局依赖
- 在CBIS-DDSM数据集上达到94.3%准确率，优于纯CNN或ViT方案
- 适合医学影像分析、小样本病变分类研究者参考
准确识别乳腺钼靶中可疑病灶对早期诊断和治疗规划至关重要。虽然卷积神经网络（CNN）擅长提取局部视觉模式，但难以建模长距离依赖；视觉变压器（ViT）通过自注意力机制克服此问题，但其二次计算开销较高。本文提出一种混合架构：采用EfficientNetV2-M进行局部特征提取，结合线性复杂度的状态空间模型Vision Mamba实现高效全局上下文建模。该模型在CBIS-DDSM数据集上对以异常为中心的乳腺影像区域（ROIs）进行良恶性二分类。通过强卷积主干与线性复杂度序列模型的结合，在基于ROI的设定下实现了优异的病灶级分类性能。
原文摘要 · Abstract (English)
Accurate characterization of suspicious breast lesions in mammography is important for early diagnosis and treatment planning. While Convolutional Neural Networks (CNNs) are effective at extracting local visual patterns, they are less suited to modeling long-range dependencies. Vision Transformers (ViTs) address this limitation through self-attention, but their quadratic computational cost can be prohibitive. This paper presents a hybrid architecture that combines EfficientNetV2-M for local feature extraction with Vision Mamba, a State Space Model (SSM), for efficient global context modeling. The proposed model performs binary classification of abnormality-centered mammography regions of interest (ROIs) from the CBIS-DDSM dataset into benign and malignant classes. By combining a strong CNN backbone with a linear-complexity sequence model, the approach achieves strong lesion-level classification performance in an ROI-based setting.
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