标题： 基于混合深度学习和手工特征融合的乳腺X光片乳腺癌分类 显示英文标题

标题： Hybrid Deep Learning and Handcrafted Feature Fusion for Mammographic Breast Cancer Classification

摘要： 从乳腺X线摄影中自动分类乳腺癌仍然是一项重大挑战，因为良性和恶性组织之间的区别很微妙。 在本工作中，我们提出了一种混合框架，将ResNet-50主干的深度卷积特征与手工设计的描述符和基于Transformer的嵌入相结合。 使用CBIS-DDSM数据集，我们对我们的ResNet-50基线进行基准测试 （AUC：78.1%），并证明将手工特征与深度ResNet-50和DINOv2特征融合可将AUC提高到79.6%（设置d1），召回率峰值为80.5%（设置d1），F1分数最高为67.4%（设置d1）。 我们的实验表明，手工特征不仅补充了深度表示，还超越了基于Transformer的嵌入提高了性能。 这种混合融合方法实现了与最先进方法相当的结果，同时保持了架构的简洁性和计算效率，使其成为临床决策支持的实用而有效的解决方案。 显示英文摘要

摘要： 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.