M3D-Net：空间上下文、特征复用与差分注意力的层次化协调用于乳腺X线摄影分类
M3D-Net: Hierarchical Coordination of Spatial Context, Feature Reuse, and Differential Attention for Mammography Classification
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中文总结 AI 辅助
M3D-Net通过层次化协调空间上下文、特征复用与差分注意力，在乳腺X线摄影分类中实现高准确率，验证准确率达97.78%和80.39%。
中文摘要 AI 辅助
乳腺图像分类需要局部细节和全局组织上下文，然而随着表示层次的加深，这些线索可能会减弱。我们提出了M3D-Net，一种乳腺X线摄影编码器，通过分辨率感知的算子放置，层次化地协调多尺度坐标注意力、有界动态特征复用和差分注意力。阶段内检索保留了对早期特征的访问，坐标感知聚合整合了局部和全局上下文，而差分注意力在粗分辨率下运行。我们在AISSLab乳腺X线摄影上评估了仅图像分类，并在BrEaST超声上评估了适应性的图像-临床模型。与EdgeNeXt、RepViT和TransXNet相比，所提出的实现达到了最高的记录验证准确率和后期训练准确率，以及最低的端点交叉熵损失。验证准确率分别达到97.78%和80.39%。这些结果支持在乳腺影像设置中进一步评估层次化协调；重复种子、组件控制和独立评估仍然是必要的。
英文摘要
Breast image classification requires local detail and global tissue context, yet these cues can weaken as representations deepen. We present M3D-Net, a mammography encoder that hierarchically coordinates multi-scale coordinate attention, bounded dynamic feature reuse, and differential attention through resolution-aware operator placement. Within-stage retrieval preserves access to earlier features, coordinate-aware aggregation integrates local and global context, and differential attention operates at coarse resolutions. We evaluate image-only classification on AISSLab mammography and an adapted image--clinical model on BrEaST ultrasound. Against EdgeNeXt, RepViT, and TransXNet, the proposed implementations achieve the highest recorded validation accuracy and late-training accuracy, with the lowest endpoint cross-entropy loss. Validation accuracies reach 97.78\% and 80.39\%, respectively. These results support further evaluation of hierarchical coordination across breast imaging settings; repeated-seed, component-controlled, and independent evaluations remain necessary.
发表机构
- Shenzhen Loop Area Institute（深圳河套学院）
- The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））
- Shenzhen Research Institute of Big Data（深圳市大数据研究院）
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