## 1 Introduction  
Approximately 50 million people worldwide have dementia, and nearly 10 million new cases occur each year. This number is expected to increase to 82 million by 2030 and 152 million by 2050 [1, 2]. Alzheimer's disease (AD), which is present in 70% of patients with dementia, is the most prevalent dementia-causing illness. It degrades memory and, thinking skills and eventually renders a person unable to maintain an independent life [3]. From a neuropathological point of view, the main factor responsible for the symptoms of AD are intracellular neurofibrillary tangles and extracellular amyloid plaques [4, 5, 6, 7]. Positron emission tomography (PET) is an ultrasensitive and non-invasive molecular imaging technique used to detect functional activity within organs that are expected to be disease-related by observing the spatiotemporal distribution of radiotracers [8]. Currently, tau-PET, which observes neurofibrillary tangles, is only used for research purposes [9], but amyloid-PET, which has been approved by the Food and Drug Administration, is used as a clinically important indicator in the diagnosis of dementia [10]. In particular, as specific changes in beta amyloid (A\(\beta\)) deposition within the brain are reported to occur earlier than changes in glucose metabolism, gross cortical atrophy, or other biomarkers, PET and related techniques are expected to provide important biomarkers that can be used for early diagnosis and prediction of AD [4; 5]. In analytical research for medical imaging, statistical analysis and machine learning (ML) represent informative analytical techniques that have provided new understanding and insight into the pathological characteristics of disease [11; 12]. In particular, deep learning (DL) technology, which is a type of ML, has recently successfully been applied to various medical imaging techniques, and has demonstrated its potential to address diverse problems such as classification [13; 14], segmentation [15; 16], detection [17; 18; 19], and reconstruction [20; 21; 22]. Most DL-based models achieve high abstraction or predict posterior probabilities for a certain class by finding the proper combination of non-linear operations. In this process, the deeply stacked model parameters connecting the input to the output (deep neural network) are fitted to \"appropriate training set\" to create a function that is general enough to make predictions. Therefore, in DL technology, the generalization performance encourages the practitioner to obtain (1) sufficient numbers, (2) balanced numbers for each class distribution, and (3) correctly labeled training sets for supervised learning problems [13; 18]. However, in most research environments, including medicine, it is difficult to obtain large number of relevant and highly qualified dataset that can provide these rich experiences. As a result, the DL research community has also conducted a variety of studies in which a given problem with constraints was solved using a small number of training datasets or datasets with incomplete or roughly drawn labels [16; 23; 24]. Augmenting a given training data, which is known as data augmentation (DA), is frequently used across domains to improve the generalization capabilities of DL models. DA techniques have been utilized in various DL-based image classification tasks because they are intuitive and easy to implement [25; 26]. The conventional DA for image data repeatedly applies a pre-defined operation (e.g., translation, rotation, or mirroring) to the input images in a training set, which allows the target model to be trained to learn additional features that are robust to changes in the operation. If the image generated as a result of such a DA operation is sufficiently homogeneous with the training data, it could also be expected to prevent overfitting caused by a relatively heavy DL-based model compared to the size of the insufficient training set [24]. However, there are some limitations to consider before applying the existing conventional DA techniques to medical images.