## 3 Materials and Methods
### Data Preprocessing  
The data preprocessing in this study was performed using in-house MATLAB software (MATLAB 2023a; The MathWorks, Inc., (Natick, MA, USA)) and a desktop computer with an NVIDIA RTX A6000 graphics card (Nvidia Corporation, Santa Clara, CA, USA). Table 2 shows the specifications of the computers used for image processing in this study. In this study, as shown in Figure 1, high-resolution mammography was divided into small patches, and images that preserved the resolution of the original images were used for image classification to identify whether calcification was present in the images within the patches using a deep learning image classifier. In this study, we used a pretrained ResNet for transfer learning in ImageNet. To achieve the pixel size of 224 \(\times\) 224 pixels, which is the input image size for ResNet18, ResNet50, and ResNet101, 212 mammography images were divided into 224 \(\times\) 224-pixel size. The right breast image was divided into 224-pixel patches by starting from the bottom right side, and the left breast image was divided into 224-pixel patches by starting from the bottom left side; the missing pixels were filled with zeros. The 15,049 patches were reviewed visually by four radiologists with 24, 14, 6, and 1 years of experience. These radiologists were certified by the Japan Central Organization on Quality Assurance of Breast Cancer Screening. As shown in Table 3, the presence or absence of calcification was classified by consensus. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Right**} & \multicolumn{2}{c}{**Left**} \\ \cline{2-5}  & **CC a** & **Image** & **MLO b** & **Image** & **CC Image** & **MLO Image** \\ \hline No. of images & 53 & 51 & 52 & 56 \\ \hline \hline \end{tabular}  
* Craniocaudal; b mediolateral oblique.  
\end{table} 
Table 1: Number of mammography images in this study. \begin{table}
\begin{tabular}{c c} \hline \hline
**Environment** & **Contents** \\ \hline Software & MATLAB 2023a (Mathworks) \\ OS & Windows 11 \\ CPU & Intel core i9-10980 XE 3.0 GHz \\ GPU & NVIDIA RTX A6000 48 GB x 2 \\ Memory & DDR4 2933 Quad-Channel 64 GB \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Software and equipment used in this study. We performed fivefold cross-validation by dividing the generated patches into five subsets, A through E. The number of mammographic images in each subset was kept similar to avoid including mammographic images of the same patient in different subsets. Table 4 shows a summary of the subset structure. Five datasets, folds 1 through 5, were created based on the subsets, with the training data for fold 1 being subsets B through E and the test data being subset A. The other datasets were created in the same way. Table 5 shows each of the datasets. Since a classifier created by training on unbalanced training data may be biased toward classes with a large number of images in the training data, a model created on training data with many images without calcification may be more easily classified as having no calcification, leading to missing calcification [32]. Therefore, we performed data augmentation on the training data in each dataset to make the number of images with and without calcification equivalent. First, the training data patches with calcification were rotated by \(90^{\circ},180^{\circ}\), and \(270^{\circ}\). Then, the original and rotated patch was flipped. Next, \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Subset**} & \multirow{2}{*}{**No. of Patients**} & \multirow{2}{*}{**No. of Mammography**} & \multicolumn{2}{c}{**Calcification in the Patch**} \\ \cline{3-4}  & & & **Images** & **Yes** & **No** \\ \hline A & 16 & 43 & 194 & 3352 \\ B & 16 & 42 & 131 & 3001 \\ C & 17 & 41 & 238 & 2329 \\ D & 15 & 43 & 196 & 2989 \\ E & 17 & 43 & 270 & 2349 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Detail of images and calcification in patches within subset. Figure 1: Schematic diagram of the training and evaluation from patch imaging of mammograms.