## Materials and Methods
### Target Model to Enhance with Generated Set  
Before elucidating the design of a generative model in Section 2.4, we first defined the target model for which the images created from the generative model in this experiment will be trained. Previous studies [43; 44] have shown that the performance of the ML/DL-based classification system for the A\(\beta\) distribution on FBB amyloid PET image data obtained from DAUH was 92.38% and 93.37%, respectively. Brain images generated using various modalities such as Magnetic Resonance Imaging (MRI), CT, and PET maintain spatial, and, depending on the conditions, temporal information of more than 3-dimensions. Therefore, various designs can be adopted depending on the features and pathological characteristics of the target lesion [45]. When evaluating PET images for the presence of FBB amyloid, the nuclear medicine physician makes a reading decision based on the contrast of gray matter observed through the axial plane of the FBB A\(\beta\) PET. Therefore, in a previous study [43; 44; 46], the BAPL score of a given FBB PET was estimated based on the A\(\beta\) distributions found at each axial level, also known as regional cortical tracer uptake (RCTU), instead of extracting the features from the 3D information according to the current process used by physicians.  
In this experiment, we used the main method described in previous studies as target classifier to observe the effectiveness of GAN-based DA with visual and quantitative similarity. Therefore, the object classifier used in this experiment consists of a feature extractor to reduce features from the 2D axial plane and a classifier to predict the A\(\beta\) distribution from the extracted features. We used DenseNet [47], a well-known convolutional neural network (CNN) structure, as the feature extractor, and the support vector machine (SVM) [48] and neural network (NN) as the classifiers. Figure 2 shows the simplified structure of the target model used in our experiment. Transfer learning is a technique that applies a model that has learned data in a specific field to similar or completely different fields, and is used in medical image classification using DL-based classifiers to report interesting results [13; 14]. It is a way to reuse the weights of a finetuned CNN model that are mostly trained with ImageNet datasets [49]. In particular, In a case that an input medical image is a originally gray scale (e.g., ultrasonography, MRI, and PET), previous studies which use the conversion of gray to RGB reports feasible performance even with an unknown artifacts and increased complexity [50; 51]. Although the input of the target model was FBB PET images which is originally a gray scale version of a real PET image, the channels of the input data were transformed into a color channel using the OpenCV-python library to match the channel size of the finetuned DenseNet model for a continuity and reproducibility of previous studies [43; 44; 46]. Target model training and model selection validation were performed using 4-fold nested cross-validation and Bayesian optimization for SVM. The search space for hyper-parameters for SVM was set to kernel functions in (Linear, RBF, Poly), C in [1; 100], gamma in [0.0001, 0.1], and for NN was deterministically set to 3 hidden layers with 128,128, and 64 nodes, respectively; Adam optimizer with \(\beta_{1}\) = 0.9, \(\beta_{2}\) = 0.999, and without decay; 300 epochs; and learning rate 0.00005.  
Figure 2: Target model scheme to measure the generalization performance after data augmentation based on the generative adversarial network.