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, 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 AUCROCs
> 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.