PDF(Pubmed)
Abstract：
BACKGROUND: Breast arterial calcifications (BAC) are common incidental findings on routine mammograms, which have been suggested as a sex-specific biomarker of cardiovascular disease (CVD) risk. Previous work showed the efficacy of a pretrained convolutional network (CNN), VCG16, for automatic BAC detection. In this study, we further tested the method by a comparative analysis with other ten CNNs.
METHODS: Four-view standard mammography exams from 1,493 women were included in this retrospective study and labeled as BAC or non-BAC by experts. The comparative study was conducted using eleven pretrained convolutional networks (CNNs) with varying depths from five architectures including Xception, VGG, ResNetV2, MobileNet, and DenseNet, fine-tuned for the binary BAC classification task. Performance evaluation involved area under the receiver operating characteristics curve (AUC-ROC) analysis, F1-score (harmonic mean of precision and recall), and generalized gradient-weighted class activation mapping (Grad-CAM++) for visual explanations.
RESULTS: The dataset exhibited a BAC prevalence of 194/1,493 women (13.0%) and 581/5,972 images (9.7%). Among the retrained models, VGG, MobileNet, and DenseNet demonstrated the most promising results, achieving AUC-ROCs > 0.70 in both training and independent testing subsets. In terms of testing F1-score, VGG16 ranked first, higher than MobileNet (0.51) and VGG19 (0.46). Qualitative analysis showed that the Grad-CAM++ heatmaps generated by VGG16 consistently outperformed those produced by others, offering a finer-grained and discriminative localization of calcified regions within images.
CONCLUSIONS: Deep transfer learning showed promise in automated BAC detection on mammograms, where relatively shallow networks demonstrated superior performances requiring shorter training times and reduced resources.
CONCLUSIONS: Deep transfer learning is a promising approach to enhance reporting BAC on mammograms and facilitate developing efficient tools for cardiovascular risk stratification in women, leveraging large-scale mammographic screening programs.
CONCLUSIONS: • We tested different pretrained convolutional networks (CNNs) for BAC detection on mammograms. • VGG and MobileNet demonstrated promising performances, outperforming their deeper, more complex counterparts. • Visual explanations using Grad-CAM++ highlighted VGG16\'s superior performance in localizing BAC.
METHODS: Four-view standard mammography exams from 1,493 women were included in this retrospective study and labeled as BAC or non-BAC by experts. The comparative study was conducted using eleven pretrained convolutional networks (CNNs) with varying depths from five architectures including Xception, VGG, ResNetV2, MobileNet, and DenseNet, fine-tuned for the binary BAC classification task. Performance evaluation involved area under the receiver operating characteristics curve (AUC-ROC) analysis, F1-score (harmonic mean of precision and recall), and generalized gradient-weighted class activation mapping (Grad-CAM++) for visual explanations.
RESULTS: The dataset exhibited a BAC prevalence of 194/1,493 women (13.0%) and 581/5,972 images (9.7%). Among the retrained models, VGG, MobileNet, and DenseNet demonstrated the most promising results, achieving AUC-ROCs > 0.70 in both training and independent testing subsets. In terms of testing F1-score, VGG16 ranked first, higher than MobileNet (0.51) and VGG19 (0.46). Qualitative analysis showed that the Grad-CAM++ heatmaps generated by VGG16 consistently outperformed those produced by others, offering a finer-grained and discriminative localization of calcified regions within images.
CONCLUSIONS: Deep transfer learning showed promise in automated BAC detection on mammograms, where relatively shallow networks demonstrated superior performances requiring shorter training times and reduced resources.
CONCLUSIONS: Deep transfer learning is a promising approach to enhance reporting BAC on mammograms and facilitate developing efficient tools for cardiovascular risk stratification in women, leveraging large-scale mammographic screening programs.
CONCLUSIONS: • We tested different pretrained convolutional networks (CNNs) for BAC detection on mammograms. • VGG and MobileNet demonstrated promising performances, outperforming their deeper, more complex counterparts. • Visual explanations using Grad-CAM++ highlighted VGG16\'s superior performance in localizing BAC.
摘要：
背景：乳腺动脉钙化（BAC）是常规乳房X线照片上常见的偶然发现，已被认为是心血管疾病（CVD）风险的性别特异性生物标志物。先前的工作显示了预训练卷积网络(CNN)的有效性，VCG16，用于自动BAC检测。在这项研究中,我们通过与其他十个CNN的比较分析进一步测试了该方法。
方法：这项回顾性研究纳入了1,493名女性的四视图标准乳房X线摄影检查，并被专家标记为BAC或非BAC。比较研究是使用十一个预训练的卷积网络（CNN）进行的，这些网络具有来自包括Xception在内的五种架构的不同深度，VGG,ResNetV2、MobileNet、和DenseNet,针对二进制BAC分类任务进行了微调。性能评估涉及接受者工作特征曲线下面积（AUC-ROC）分析，F1分数(精度和召回率的调和平均值)，和广义梯度加权类激活映射(Grad-CAM++)，用于直观解释。
结果：数据集显示BAC患病率为194/1,493名女性（13.0%）和581/5,972名女性（9.7%）。在重新训练的模型中，VGG,MobileNet,DenseNet展示了最有希望的结果，在训练和独立测试子集实现AUC-ROC>0.70。在测试F1分数方面，VGG16排名第一,高于MobileNet（0.51）和VGG19（0.46）。定性分析表明，VGG16生成的Grad-CAM++热图的性能始终优于其他人生成的热图，提供图像中钙化区域的细粒度和区别性定位。
结论：深度迁移学习在乳房X线照片的自动BAC检测中显示出希望，相对较浅的网络表现出卓越的性能，需要更短的培训时间和减少的资源。
结论：深度迁移学习是一种有前途的方法，可以增强乳腺X线照片的BAC报告，并促进开发用于女性心血管危险分层的有效工具。利用大规模乳房X光检查计划。
结论：•我们测试了不同的预训练卷积网络（CNN），用于乳房X线照片上的BAC检测。•VGG和MobileNet表现出了有希望的表现，超越他们更深层次的，更复杂的同行。•使用Grad-CAM++的视觉解释突出了VGG16在本地化BAC方面的卓越性能。
方法：这项回顾性研究纳入了1,493名女性的四视图标准乳房X线摄影检查，并被专家标记为BAC或非BAC。比较研究是使用十一个预训练的卷积网络（CNN）进行的，这些网络具有来自包括Xception在内的五种架构的不同深度，VGG,ResNetV2、MobileNet、和DenseNet,针对二进制BAC分类任务进行了微调。性能评估涉及接受者工作特征曲线下面积（AUC-ROC）分析，F1分数(精度和召回率的调和平均值)，和广义梯度加权类激活映射(Grad-CAM++)，用于直观解释。
结果：数据集显示BAC患病率为194/1,493名女性（13.0%）和581/5,972名女性（9.7%）。在重新训练的模型中，VGG,MobileNet,DenseNet展示了最有希望的结果，在训练和独立测试子集实现AUC-ROC>0.70。在测试F1分数方面，VGG16排名第一,高于MobileNet（0.51）和VGG19（0.46）。定性分析表明，VGG16生成的Grad-CAM++热图的性能始终优于其他人生成的热图，提供图像中钙化区域的细粒度和区别性定位。
结论：深度迁移学习在乳房X线照片的自动BAC检测中显示出希望，相对较浅的网络表现出卓越的性能，需要更短的培训时间和减少的资源。
结论：深度迁移学习是一种有前途的方法，可以增强乳腺X线照片的BAC报告，并促进开发用于女性心血管危险分层的有效工具。利用大规模乳房X光检查计划。
结论：•我们测试了不同的预训练卷积网络（CNN），用于乳房X线照片上的BAC检测。•VGG和MobileNet表现出了有希望的表现，超越他们更深层次的，更复杂的同行。•使用Grad-CAM++的视觉解释突出了VGG16在本地化BAC方面的卓越性能。
