## 4 Discussion
The four 2.5D images used in the proposed method were all composed of three cross-section slices (axial, coronal, and sagittal) with fixed angles. Separate latent variable analysis and feature extraction were performed on each 2.5D image. However, additional cross-sectional angles may achieve higher performance than the proposed method. [PERSON] et al. achieved highly accurate aneurysm detection using nine-channel MIP-2.5D image processing, which included the three cross sections used in the proposed method plus oblique slices. On the other hand, only a lower performance than that shown here was achieved when twelve channels comprising the four 2.5D images used in this study were analyzed simultaneously as a single 2.5D image. This lower performance suggests the need to find the optimal combination of cross-sectional images. Thus, we plan to address the problem of optimizing the set of cross-sectional slices to be processed experimentally. The proposed method employs a simple CAE model to enable feature generation with a small training dataset. The experiments were conducted using a small number of training datasets to verify the robustness in situations with a small training dataset. In an experiment to classify candidate cerebral aneurysms, when the number of normal cases used for feature training was 30 or more, there was no statistically significant difference from the candidate identification performance based on training with 252 normal cases (U-test of AUC). These results showed the robustness of the proposed method in situations with a small number of training cases. The limitation of the proposed method is its poor classification performance compared to handcrafted features and features obtained via supervised learning, as shown in Tables 4 and 5. The brain aneurysm detection method by [PERSON] et al. [20] using handcrafted features was a two-stage lesion detection process in which discriminative processing using multiple features was performed on lesion candidates detected using luminance curvature features. The method achieved a sensitivity of 93.5% at five FPs per case via the use of appropriate image features for lesion candidate detection and candidate classification. This sensitivity is about 10% higher than the present experimental results. However, in the present experiment, the features in the proposed method were only used for the identification of lesion candidates. The fact that the features in the proposed method were not used to detect lesion candidates is the main reason for the difference in performance. Further comparisons should be made again using the results of the application of the features in the proposed method to the lesion candidate detection process. [PERSON] et al. also proposed a method for discriminating all local image patches containing automatically extracted vascular regions using deep supervised learning CNN [13]. Its final detection performance was 94.2% sensitivity when the number of FPs per case was 2.9. [PERSON] et al. proposed pulmonary nodule detection in CT images using deep convolutional neural networks and achieved high detection performance with a 0.790 ANODE score [21]. Although simple comparisons are difficult due to different evaluation datasets, this score is 0.39 higher than the results of the current experiment. Supervised deep learning with both normal and lesion class data is a powerful learning method that allows the derivation of feature spaces and discriminative boundaries that effectively separate both classes of data. This characteristic led to significantly higher sensitivity than our experiments. On the other hand, the methods proposed by [PERSON] et al. [13] and [PERSON] et al. [21] are likely to experience significant performance degradation if they can only be trained on a small amount of lesion data. The features obtained via the proposed method do not depend on the size of the cerebral aneurysm dataset collected for system development. This characteristic is a significant advantage of the proposed method using unsupervised learning.