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

Introduction Breast arterial calcifications (BAC) are common incidental findings on routine mammograms, whichhave been suggested as a sex-specific biomarker of cardiovascular disease (CVD) risk. Previous work showed the efficacyof a pretrained convolutional network (CNN), VCG16, for automatic BAC detection. In this study, we further testedthe 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 retrospectivestudy and labeled as BAC or non-BAC by experts. The comparative study was conducted using elevenpretrained 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 evaluationinvolved area under the receiver operating characteristics curve (AUC-ROC) analysis, F1-score (harmonic mean of precisionand 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%). Amongthe retrained models, VGG, MobileNet, and DenseNet demonstrated the most promising results, achieving AUCROCs> 0.70 in both training and independent testing subsets. In terms of testing F1-score, VGG16 ranked first, higherthan MobileNet (0.51) and VGG19 (0.46). Qualitative analysis showed that the Grad-CAM++ heatmaps generatedby VGG16 consistently outperformed those produced by others, offering a finer-grained and discriminative localizationof calcified regions within images.Conclusion Deep transfer learning showed promise in automated BAC detection on mammograms, where relativelyshallow 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 mammogramsand facilitate developing efficient tools for cardiovascular risk stratification in women, leveraging large-scale mammographicscreening 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 complexcounterparts.• Visual explanations using Grad-CAM++ highlighted VGG16’s superior performance in localizing BAC.