## Materials and Methods
### Data Acquisition and Pre-Processing  
The FBB PET/CT images used in this study were collected retrospectively from images taken at the Department of Nuclear Medicine, Dong-A University Hospital (DAUH) from November 2015 to May 2018. The Institutional Review Board of Dong-A University Hospital reviewed and approved this study protocol (DAUHIRB-17-108). Each FBB image was confirmed by a nuclear medicine physician after collection to ensure that the A\(\beta\) distribution labels were accurate. The labeling work performed for our experimental data was based on the brain amyloid plaque load (BAPL) scoring system for reading existing FBB images. Four areas of the brain including the frontal lobe, temporal lobe, parietal lobe, and posterior cingulate were observed in the axial plane and scored based on the amount of A\(\beta\) deposited on the gray matter against the white matter [35; 36]. All subjects photographed in this study received clinical diagnosis by DAUH, a neurologist. There were 298 participants in the data group, which included 160 typical A\(\beta\) negatives and 138 typical A\(\beta\) positives. Detailed demographic data are presented in Table 1. The FBB PET images used in this experiment were taken using a Biograph 40 mCT Flow PET/CT scanner (Siemens Healthcare, Knoxville, TN, USA) and reconstructed via UltraHD-PET (TrueX-TOF). The participants were photographed 90 min after an FBB (NeuraCeq, Piramal, Mumbai, India) dose of 300 mBq was intravenously injected and images were taken 20 min after Helical CT with a 0.5 s rotation time at 100 kVp and 228 mAs. The raw PET images used in this experiment were resliced from a field of view of \(408\times 408\times 168\) (mm) and stored in the DICOM format in the DAUH PACS. The pre-processing steps, including co-registration and spatial and count normalization for the brain images applied in this experiment, were performed based on statistical parametric mapping 8 [37]. Rigid co-registration was first performed on each PET image and the corresponding CT image with respect to the center. An in-house PET template was created using CT images from 21 patients without typical AD and 9 patients with typical AD along the MNI space. Spatial normalization was performed on each PET image using the generated PET template [38; 39; 40]. Then, stochastic cerebellar masks for the PET templates were obtained from PMOD3.6 (PMOD Technologies Ltd., Zurich, Switzerland) and the [PERSON] brain atlas [41], and these were used to perform count normalization based on cerebellar intensity [42]. After pre-processing, the input data for the A\(\beta\) classifier was extracted and only the 15-50 th axial images so that only the axial plane that was read by the nuclear medicine physician was examined. Finally, a \(95\times 79\times 36\) image representing the A\(\beta\) distribution of each subject was used as an input for the GAN and target classifier models. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Variable** & **Division** & **A\(\beta\) Negative** & **A\(\beta\) Positive** & **Total** & **\(p\)-Value** \\ \hline \# & data & 160 & 138 & 298 & \\  & Male & 56 & 61 & 117 & \\ Sex & Female & 104 & 77 & 181 & 0.102 \\ Age & & 67.76 \(\pm\) 9.09 & 69.56 \(\pm\) 8.07 & 68.58 \(\pm\) 8.67 & 0.0916 \\  & NC\({}^{1}\) + SCD\({}^{2}\) & 58 & 4 & 62 & \\ Diagnosis & MCI\({}^{3}\) & 74 & 25 & 99 & \textless{}0.0001 \({}^{\star}\) \\  & AD\({}^{4}\) & 28 & 109 & 137 & \\ Education(y) & & 9.27 \(\pm\) 4.23 & 10.07 \(\pm\) 4.11 & 9.64 \(\pm\) 4.19 & 0.0802 \\ K-MMSE\({}^{5}\) & & 25.24 \(\pm\) 3.77 & 20.42 \(\pm\) 4.61 & 22.98 \(\pm\) 4.82 & \textless{}0.0001 \({}^{\star}\) \\ \hline \end{tabular} \({}^{1}\) NC: Normal Control. \({}^{2}\) SCD: Subjective Cognitive Decline. \({}^{3}\) MCI: Mild Cognitive Impairment. \({}^{4}\) AD: Alzheimer’s Disease. \({}^{5}\) Korean version of Mini-Mental State Examination \({}^{\star}\): statistically significant with 99% level of confidence.  
\end{table}