融合注意力与多尺度特征，提升乳腺癌图像检测准确率。
An Innovative Framework for Breast Cancer Detection Using Pyramid Adaptive Atrous Convolution, Transformer Integration, and Multi-Scale Feature Fusion
- 用金字塔自适应空洞卷积和Transformer捕捉局部与长程特征
- 在三个数据集上达98.5%准确率，远超传统模型
- 适合医疗影像诊断系统集成，助力早期癌症筛查
乳腺癌是全球女性中最常见的癌症之一，其精准及时的诊断对改善治疗效果至关重要。本文提出一种创新框架，通过整合金字塔自适应空洞卷积（PAAC）与Transformer架构，用于检测乳腺钼靶图像中的恶性肿块。该方法采用多尺度特征融合增强良恶性组织特征提取，并结合Dice Loss与Focal Loss优化学习过程，有效降低二分类错误率，实现高精度与高效率。研究使用INbreast、MIAS和DDSM数据集，经数据增强与对比度增强预处理后统一调整为227×227像素。借助Transformer的自注意力机制捕捉长距离依赖，模型在检测癌变病灶方面表现优异，显著优于BreastNet、DeepMammo、Multi-Scale CNN、Swin-Unet和SegFormer等基线模型。最终评估结果为：准确率98.5%、敏感性97.8%、特异性96.3%、F1分数98.2%、总体精确率97.9%，验证了其在复杂场景与大规模数据下的有效性。该模型具备作为可靠高效乳腺癌诊断工具的潜力，可有效融入医疗诊断系统。
原文摘要 · Abstract (English)
Breast cancer is one of the most common cancers among women worldwide, and its accurate and timely diagnosis plays a critical role in improving treatment outcomes. This thesis presents an innovative framework for detecting malignant masses in mammographic images by integrating the Pyramid Adaptive Atrous Convolution (PAAC) and Transformer architectures. The proposed approach utilizes Multi-Scale Feature Fusion to enhance the extraction of features from benign and malignant tissues and combines Dice Loss and Focal Loss functions to improve the model's learning process, effectively reducing errors in binary breast cancer classification and achieving high accuracy and efficiency. In this study, a comprehensive dataset of breast cancer images from INbreast, MIAS, and DDSM was preprocessed through data augmentation and contrast enhancement and resized to 227x227 pixels for model training. Leveraging the Transformer's ability to manage long-range dependencies with Self-Attention mechanisms, the proposed model achieved high accuracy in detecting cancerous masses, outperforming foundational models such as BreastNet, DeepMammo, Multi-Scale CNN, Swin-Unet, and SegFormer. The final evaluation results for the proposed model include an accuracy of 98.5\%, sensitivity of 97.8\%, specificity of 96.3\%, F1-score of 98.2\%, and overall precision of 97.9\%. These metrics demonstrate a significant improvement over traditional methods and confirm the model's effectiveness in identifying cancerous masses in complex scenarios and large datasets. This model shows potential as a reliable and efficient tool for breast cancer diagnosis and can be effectively integrated into medical diagnostic systems.
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