## 1 Introduction  
Bladder cancer is the 11 th most prevalent cancer, and its global incidence is gradually increasing [1]. Most bladder cancers are urothelial carcinomas, occurring in the urothel lining of the bladder. Cystoscopy, urine cytology, and ultrasonography are the gold standard for diagnosing bladder cancer. Urine cytology examines the presence or absence of cancer cells in the excreted urine, thereby making it a non-invasive technique that can be performed repeatedly. The detection rate of this technique for advanced stages of cancer is higher than that of cystoscopy, making it extremely useful in clinical diagnosis. However, the detection sensitivity is low for early stages of cancer, and the weak cellular atypia in highly differentiated cancers makes the identification of malignant cells challenging. Therefore, in clinical practice, there is a need to develop diagnostic support technologies that can test urinary cells with high accuracy. This study aimed to develop a computer-aided diagnosis (CAD) technique [2, 3] for this purpose. This study focuses on deep learning [4, 5], an artificial intelligence technology, to provide diagnostic assistance in urine cytology. Deep learning is based on multi-layer neural network technology. In particular, convolutional neural networks (CNNs), which mimic animal vision, have been widely used for image classification, object detection, and the prediction of future events. There are many applications of medical imaging, and there have been many reports on applications with computed tomography, magnetic resonance imaging [6; 7; 8], and pathological images [9; 10; 11]. For pathological images, we have developed diagnostic support technology for lung cytology in which a CNN is used to differentiate between benign and malignant cells [12; 13; 14]. A method to input a finely cut patch image into a CNN to classify benign and malignant cells and create a malignancy map for the entire microscopic field of view was proposed [12]. The detection sensitivity and specificity for malignant cells aggregated per patch image were 94.6% and 63.4%, respectively, and those aggregated per case were 89.3% and 83.3%, respectively. During the classification of cytology images, it is important to include a wide variety of images in the training data to improve the detection efficiency. Therefore, we proposed a method to output benign and malignant cell images using a generative adversarial network (GAN), which can virtually generate images and use them as part of the training data for the CNN [13]. We also proposed a weakly supervised learning method that uses the attention mechanism to classify benign and malignant cell images on a case-by-case basis without providing correct labels for individual patch images and showed that the performance is comparable to supervised learning [14]. [PERSON] et al. attempted to automate the Paris System for reporting urinary cytology by using whole slide images in combination with cell shape analysis and deep learning, and they obtained an area under the curve (AUC) value of 0.92 for the classification of high- and low-risk groups [15]. Further, [PERSON] et al. proposed a method for risk stratification of urinary cytology using a CNN after recognizing urinary cells using image processing [16]. In these studies, the CNN model was trained by collecting the cellular images of interest. Good classification performance can be achieved when the disease has a high prevalence and sufficient data are available. However, rare diseases or unbalanced data sets such as the presence of very few malignant cells compared to benign cells can result in poor classification performance. In such cases, it would be ideal to train the CNN using cell images from different sites to enable variation learning, thereby generating a CNN model with high performance. Transfer learning and fine-tuning are techniques used for diverting CNNs to other applications.