Zhu Minjuan, Zhang Lei, Wang Lituan, Wang Zizhou, Wang Yan, Qian Guangwu
College of Computer Science, Sichuan University, Section 4, Southern 1st Ring Rd., Chengdu 610065, China.
Institute of High Performance Computing, Agency for Science, Technology and Research (A*STAR), Singapore 138632, Singapore.
Bioengineering (Basel). 2025 Mar 21;12(4):325. doi: 10.3390/bioengineering12040325.
The early and accurate detection of breast lesions through mammography is crucial for improving survival rates. However, the existing deep learning-based methods often rely on costly pixel-level annotations, limiting their scalability in real-world applications. To address this issue, a novel local extremum mapping (LEM) mechanism is proposed for mammogram classification and weakly supervised lesion localization. The proposed method first divides the input mammogram into multiple regions and generates score maps through convolutional neural networks. Then, it identifies the most informative regions by filtering local extrema in the score maps and aggregating their scores for final classification. This strategy enables lesion localization with only image-level labels, significantly reducing annotation costs. Experiments on two public mammography datasets, CBIS-DDSM and INbreast, demonstrate that the proposed method achieves competitive performance. On the INbreast dataset, LEM improves classification accuracy to 96.3% with an AUC of 0.976. Furthermore, the proposed method effectively localizes lesions with a dice similarity coefficient of 0.37, outperforming Grad-CAM and other baseline approaches. These results highlight the practical significance and potential clinical applications of our approach, making automated mammogram analysis more accessible and efficient.
通过乳房X光检查早期准确地检测乳腺病变对于提高生存率至关重要。然而，现有的基于深度学习的方法通常依赖于成本高昂的像素级标注，限制了它们在实际应用中的可扩展性。为了解决这个问题，提出了一种新颖的局部极值映射（LEM）机制用于乳房X光图像分类和弱监督病变定位。所提出的方法首先将输入的乳房X光图像划分为多个区域，并通过卷积神经网络生成得分图。然后，通过过滤得分图中的局部极值并汇总其得分进行最终分类，来识别最具信息的区域。这种策略仅使用图像级标签就能实现病变定位，显著降低了标注成本。在两个公共乳房X光数据集CBIS-DDSM和INbreast上的实验表明，所提出的方法取得了具有竞争力的性能。在INbreast数据集上，LEM将分类准确率提高到96.3%，曲线下面积（AUC）为0.976。此外，所提出的方法以0.37的骰子相似系数有效地定位病变，优于Grad-CAM和其他基线方法。这些结果突出了我们方法的实际意义和潜在的临床应用，使自动乳房X光图像分析更易于实现且高效。
