## 3 Materials and Methods
### Data Preprocessing  
The 212 mammogram images (average matrix size: 2330 \(\times\) 1310 pixels) were divided into 224 \(\times\) 224 pixel patches, which is the input size of ResNet. The right breast image was divided into 224 pixel patches starting from the lower right side, the left breast image was \begin{table}
\begin{tabular}{l c} \hline \hline
**Previous Methods for Mammography** & **Novelty for This Study** \\ \hline -Detection of soft tissue findings & Utilization of semi-supervised learning for mammography \\ -Detection rate of 80–90\% [4] & -Reduction of labeling effort \\ -Detection of calcification clusters [8; 10] & -Improvement in classification accuracy \\ -Difficulty in detecting microcalcifications [4; 9] & -Investigation of classification probability thresholds \\ -Information loss due to compression & -New approach to classification accuracy \\ -High labeling effort & -Combination of patch division and semi-supervised learning \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Novelty for this study compared to previous methods. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Right** & **Left** \\ \cline{2-4}  & **CC a Image** & **MLO b Image** & **CC Image** & **MLO Image** \\ \hline Number of images & 173 & 174 & 183 & 182 \\ \hline \hline \end{tabular}  
* Craniocaudal; b mediolateral oblique.  
\end{table} 
Table 2: 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 i9-10920X 3.50 GHz \\ GPU & NVIDIA Quadro P5000 16 GB \(\times\) 4 \\ Memory & DIMM 2666 MHz 64.0 GB \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Software and equipment used in this study. divided into 224 pixel patches starting from the lower left side, and the missing areas were filled with zeros. The 15,049 patches were then visually classified according to the presence or absence of calcification, as shown in Table 4. The calcification in the patches was visually classified and confirmed by four radiological technologists, each with varying levels of experience: one with more than 20 years, one with 10 years, one with 5 years, and one with less than 5 years of technician licensure. The criteria used for the presence or absence of calcification are identical to the data published in a previously submitted paper [22]. Among the classified patches, the patches generated from 169 mammographic images were used as training data, and the patches generated from the remaining 43 mammographic images were used as test data. Table 5 shows a breakdown of the number of patches in the training and test data. A classifier trained on unbalanced training data is likely to be biased toward the class with the largest number of images in the training data. Therefore, a model created with training data that have more patches without calcification than patches with calcification is more likely to classify patches as without calcification, which may lead to missing calcifications. Therefore, data augmentation was performed. The patches without calcification were inverted and doubled. The patches with calcification were rotated by 90\({}^{\circ}\), 180\({}^{\circ}\), and 270\({}^{\circ}\) respectively. The original patch was inverted, and the inverted patch was rotated by 90\({}^{\circ}\), 180\({}^{\circ}\), and 270\({}^{\circ}\), respectively. The pixel luminance of each patch was then multiplied by 0.5 and 0.75 to expand the number of patches with calcification by a factor of 24. Figure 1 shows a schematic of data augmentation, and Table 6 below shows the changes in image data resulting from data augmentation. In this study, we used a pretrained ResNet 50 [32] for transfer learning in ImageNet. The post-expansion training data were trained on ResNet50 to create a classifier that performs a two-class classification of the presence or absence of calcification. Table 7 shows the parameters used to create the classifiers.