# Two-Stage Deep Learning Method for Breast Cancer Detection Using High-Resolution Mammogram Images
## 3 Methodology
### RoI Generation
Among all compared methods, only our method can directly be used to assist radiologists, since we train our classifier to identify standard BI-RADS categories. By automatically generating small patches from the original mammogram, our method can focus on smaller RoI to improve the detection accuracy. Moreover, appropriate data processing tools contributed to accuracy improvement, especially, for the INbreast dataset (due to intensity and contrast differences). Our model achieved the highest accuracy and AUC scores of 0.95 and 0.97, respectively. In addition, with the support of a classification network, we improved the overall accuracy of the model. Although it did not contribute a lot for the INbreast dataset (due to binary classification), in our private dataset, the model's mAP is improved from 0.92 (without the CNN model) to 0.94 (with CNN) across six BI-RADS categories. This proves that our CNN based-model helps identify key feature differences for intra-classes (e.g., BI-RADS 4a vs. 4b). In Table 5, corresponding results of compared methodologies are obtained by their study reports. Among methodologies that use the whole mammogram images, the Di-aGRAM method proposed by [PERSON] et al. [17] achieved the best results. However, we believe more and more research studies should focus on better ways of generating smaller RoIs and avoid using the whole mammogram, as the size of the medical imaging gets bigger and bigger with the advancement of high-resolution input modalities. Clear evidence can be seen from the resolution and the quality differences between the DDSM database, which contains digitized film mammograms and INbreast, which is fully digital. In comparison, mammograms in our collected dataset have twice the resolution of INbreast images. ## 5 Conclusions

In this paper, we propose a two-stage deep learning method for breast cancer detection using high-resolution mammograms. Our method does not require any human supervision or manually annotated RoIs to perform accurate detection for in-the-wild data. Experimental results on the collected mammogram dataset demonstrate significant improvement over the original Faster R-CNN model. Our model does not only improve detection accuracy for BI-RADS categories but also improves classification accuracy by combining output scores with a specifically-designed tumor classifier. In addition, due to the contribution of generated patches, the model reduces false detections. Using square patch generation, bounding box mapping, and duplication removal algorithms, our method could achieve near-optimal detection accuracy on the labeled test data. Moreover, through extended \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Network** & **End-to-End** & **Fully CAD** & **Accuracy** & **AUC** \\ \hline [PERSON] et al. [30] & CNN & No & No & 0.95 & 0.91 \\ [PERSON] et al. [31] & MIL & Yes & No & 0.90 & 0.89 \\ [PERSON] et al. [17] & GAN + CNN & Yes & No & 0.935 & 0.925 \\ [PERSON] et al. [15] & SSD + cGAN & Yes & No & 0.80 & - \\ [PERSON] et al. [10] & CNN & Yes & No & - & 0.95 \\ Our model & R-CNN + CNN & Yes & Yes & 0.95 & 0.97 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The overall performance comparison with existing methods on the INbreast dataset. comparisons with the existing breast cancer diagnosis methods on the publicly available INbreast dataset, we showed the superiority of our model. Our proposed model can be used to assist radiologists in real-world breast cancer screening and reporting. Even though our CNN-based classifier improved the overall performance, there are still a few misclassified intra-class scores (BI-RADS 4a and 4b). In the future work, we aim to design better standalone classifiers by thoroughly experimenting on different network architectures. Conceptualization, B.I. and J.-Y.K. ; methodology, B.I. ; validation, B.I. ; formal analysis, B.I. ; investigation, B.I. ; resources, J.-Y.K. ; data curation, B.I. ; writing--original draft preparation, B.I. ; writing--review and editing, B.I. and J.-Y.K. ; visualization, B.I.