Deep transfer learning for detection of breast arterial calcifications on mammograms: a comparative study.

IF 3.7 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING European Radiology Experimental Pub Date : 2024-07-15 DOI:10.1186/s41747-024-00478-6
Nazanin Mobini, Davide Capra, Anna Colarieti, Moreno Zanardo, Giuseppe Baselli, Francesco Sardanelli
{"title":"Deep transfer learning for detection of breast arterial calcifications on mammograms: a comparative study.","authors":"Nazanin Mobini, Davide Capra, Anna Colarieti, Moreno Zanardo, Giuseppe Baselli, Francesco Sardanelli","doi":"10.1186/s41747-024-00478-6","DOIUrl":null,"url":null,"abstract":"<p><strong>Introduction: </strong>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.</p><p><strong>Material and methods: </strong>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, F<sub>1</sub>-score (harmonic mean of precision and recall), and generalized gradient-weighted class activation mapping (Grad-CAM++) for visual explanations.</p><p><strong>Results: </strong>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 F<sub>1</sub>-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.</p><p><strong>Conclusion: </strong>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.</p><p><strong>Relevance statement: </strong>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.</p><p><strong>Key points: </strong>• 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.</p>","PeriodicalId":36926,"journal":{"name":"European Radiology Experimental","volume":"8 1","pages":"80"},"PeriodicalIF":3.7000,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11247067/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"European Radiology Experimental","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1186/s41747-024-00478-6","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}
引用次数: 0

Abstract

Introduction: 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.

Material and 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.

Conclusion: 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.

Relevance statement: 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.

Key points: • 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.

Abstract Image

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
用于检测乳房 X 光照片上乳腺动脉钙化的深度传输学习:一项比较研究。
简介乳房动脉钙化(BAC)是常规乳房 X 光检查中常见的偶然发现,被认为是心血管疾病(CVD)风险的性别特异性生物标志物。之前的研究表明,预训练卷积网络(CNN)VCG16 对自动检测 BAC 非常有效。在本研究中,我们通过与其他十种 CNN 的比较分析进一步测试了该方法:本回顾性研究纳入了 1,493 名女性的四视角标准乳腺 X 光检查结果,并由专家将其标记为 BAC 或非 BAC。比较研究使用了 11 个经过预训练的卷积网络(CNN),这些网络来自 Xception、VGG、ResNetV2、MobileNet 和 DenseNet 等五种架构,深度各不相同,并针对二元 BAC 分类任务进行了微调。性能评估包括接受者操作特征曲线下面积(AUC-ROC)分析、F1-分数(精确度和召回率的调和平均值)以及用于视觉解释的广义梯度加权类激活映射(Grad-CAM++):数据集显示,BAC 发生率为 194/1,493 名女性(13.0%)和 581/5,972 幅图像(9.7%)。在重新训练的模型中,VGG、MobileNet 和 DenseNet 的结果最有希望,在训练和独立测试子集中的 AUC-ROC 均大于 0.70。在测试 F1 分数方面,VGG16 排名第一,高于 MobileNet(0.51)和 VGG19(0.46)。定性分析显示,VGG16 生成的 Grad-CAM++ 热图始终优于其他生成的热图,能对图像中的钙化区域进行更精细、更有辨别力的定位:深度迁移学习在乳房 X 光照片的 BAC 自动检测中大有可为,其中相对较浅的网络表现出了卓越的性能,需要更短的训练时间和更少的资源:深度迁移学习是一种很有前途的方法,它能提高乳房 X 光照片上 BAC 的报告率,并有助于开发高效的工具,利用大规模乳房 X 光照片筛查计划对女性进行心血管风险分层:- 我们测试了不同的预训练卷积网络 (CNN),以检测乳房 X 光照片上的 BAC。- VGG和MobileNet表现出了良好的性能,超过了更深、更复杂的同类产品。- 使用 Grad-CAM++ 进行的可视化解释凸显了 VGG16 在定位 BAC 方面的卓越性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
European Radiology Experimental
European Radiology Experimental Medicine-Radiology, Nuclear Medicine and Imaging
CiteScore
6.70
自引率
2.60%
发文量
56
审稿时长
18 weeks
期刊最新文献
An AI deep learning algorithm for detecting pulmonary nodules on ultra-low-dose CT in an emergency setting: a reader study. Evaluation of pulmonary artery pressure, blood indices, and myocardial microcirculation in rats returning from high altitude to moderate altitude. Image biomarkers and explainable AI: handcrafted features versus deep learned features. Technical feasibility of automated blur detection in digital mammography using convolutional neural network. Quantification of breast biopsy clip marker artifact on routine breast MRI sequences: a phantom study.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1