融合深度特征与手工特征，提升乳腺癌分类准确率
Hybrid Deep Learning and Handcrafted Feature Fusion for Mammographic Breast Cancer Classification
- 用ResNet-50和DINOv2提取深层特征，结合手工特征进行融合
- 在CBIS-DDSM数据集上AUC达79.6%，最高召回率80.5%
- 方法简单高效，适合临床辅助诊断场景
由于良性和恶性组织间差异细微，基于乳腺钼靶图像的自动化乳腺癌分类仍是重大挑战。本文提出一种混合框架，结合基于ResNet-50主干网络的深度卷积特征、手工设计描述符以及基于Transformer的嵌入表示。在CBIS-DDSM数据集上，我们评估了ResNet-50基线模型（AUC: 78.1%），并证明将手工特征与深度ResNet-50及DINOv2特征融合后，AUC提升至79.6%（设定d1），召回率峰值达80.5%（设定d1），F1分数最高为67.4%（设定d1）。实验表明，手工特征不仅补充了深度表征，还进一步提升了性能，超越仅使用Transformer嵌入的结果。该混合融合方法在保持架构简洁与计算高效的同时，达到与当前最先进方法相当的效果，是临床决策支持的实用有效方案。
原文摘要 · Abstract (English)
Automated breast cancer classification from mammography remains a significant challenge due to subtle distinctions between benign and malignant tissue. In this work, we present a hybrid framework combining deep convolutional features from a ResNet-50 backbone with handcrafted descriptors and transformer-based embeddings. Using the CBIS-DDSM dataset, we benchmark our ResNet-50 baseline (AUC: 78.1%) and demonstrate that fusing handcrafted features with deep ResNet-50 and DINOv2 features improves AUC to 79.6% (setup d1), with a peak recall of 80.5% (setup d1) and highest F1 score of 67.4% (setup d1). Our experiments show that handcrafted features not only complement deep representations but also enhance performance beyond transformer-based embeddings. This hybrid fusion approach achieves results comparable to state-of-the-art methods while maintaining architectural simplicity and computational efficiency, making it a practical and effective solution for clinical decision support.
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