###### Abstract  
There are growing expectations for AI computer-aided diagnosis: computer-aided diagnosis (CAD) systems can be used to improve the accuracy of diagnostic imaging. However, it is not easy to collect large amounts of disease medical image data with lesion area annotations for the supervised learning of CAD systems. This study proposes an unsupervised local image feature extraction method running without such disease medical image datasets. Local image features are one of the key determinants of system performance. The proposed method requires only a normal image dataset that does not include lesions and can be collected easier than a disease dataset. The unsupervised features are extracted by applying multiple convolutional autoencoders to analyze various 2.5-dimensional images. The proposed method is evaluated by two kinds of problems: the detection of cerebral aneurysms in head MRA images and the detection of lung nodules in chest CT images. In both cases, the performance is high, showing an AUC of more than 0.96. These results show that the proposed method can automatically learn features that are useful for lesion recognition from lesion-free normal data, regardless of the type of image or lesion. computer-aided diagnosis; unsupervised learning; convolutional autoencoder; 2.5-dimensional image analysis +
Footnote †: journal: Medical Image Analysis  
[PERSON] et al. reported that using a CAD system for CT image diagnosis of lung nodules improved the detection sensitivity of inexperienced radiologists from 20.9% to 38.0% and reduced diagnosis time by 11.3% [4]. [PERSON] et al. also reported that the use of a CAD system for CT image diagnosis of obliterating lung cancer improved the area under the receiver operating characteristic (ROC) curve (AUC) of radiologists' diagnostic accuracy from 0.763 to 0.854 [5]. [PERSON] et al. confirmed that using a CAD system to detect cerebral aneurysms on MR angiography (MRA) could improve the AUC from 0.931 to 0.983 [6]. [PERSON] et al. also reported an improvement in breast cancer detection accuracy of mammography with concurrent use of a CAD system [7]. Both reports demonstrate the clinical usefulness of CAD systems; thus, further development of CAD technology is warranted. In CAD systems, local image features are one of the key elements of system performance [8]. The typical processing steps of a CAD system are described using the example of a lesion detection system, which is one of the most common types of CAD systems. The lesion detection CAD system generally consists of pre-processing input data, the extraction of organ regions where the target lesion occurs, detecting lesion candidates, and classifying lesion candidates. High-quality local image features are essential for structuring sensitive lesion candidate detection and accurate lesion candidate classification. In the past, the features used in these processes were mainly custom-made and implemented. In recent years, many studies have implemented supervised deep learning methods, where one could simultaneously learn both classification and image feature extraction [9]. This simultaneous learning, which often leads to optimal image features for classification, improves classification/detection performances. On the other hand, it is not easy to collect large amounts of medical image data for supervised deep learning of CAD systems [10]. When developing systems for rare diseases, it is often necessary to do so with almost no data on the target disease. In addition, machine-learning-based CAD system development requires annotation, which is the creation of teacher labels for lesion areas on the images. This annotation requires a high level of medical knowledge and can only be conducted with the help of experienced radiologists. Many studies and developments have recently used medical image open databases, such as the LIDC-IDRI (lung image database consortium image collection), for CAD systems [11]. However, there are many diseases for which no such database exists.