# Semantic Decomposition and Anomaly Detection of Tympan Membrane Endoscopic Images
## 1 Introduction  
Otitis Media (OM) is one of the most common disorders in children worldwide and studies indicate that over 80% of infants up to the age of three might have OM, with many of these cases likely to recur [1, 2]. Specifically, cholesteatoma is an ear lesion caused by the abnormal collection of skin cells, which gradually expands, resulting in the erosion of neighboring bone structures. It can cause various complications, including hearing loss, vestibular dysfunction, and facial paralysis. The causes of the disease vary, including perforation and retraction. Misdiagnosing middle ear disease can result in undertreatment or overtreatment, resulting in severe adverse consequences [3]. Additionally, many OM patients have been prescribed antibiotics, which increase resistance and, therefore, require a precise diagnosis. However, the average diagnostic accuracy of pediatricians and otolaryngologists is 50% and 73%, respectively [4]. Moreover, otolaryngology diagnosis is conducted by a visual mechanism for the otoloscope. Therefore, diagnosis and precision may be biased, depending on the person diagnosing [5]. Therefore, using Artificial Intelligence (AI) in otolaryngology is reasonable. Recently, with the development of deep learning, more research has used AI technology for medical data [6] and more studies on middle ear disorders have been conducted [7]. [PERSON] al. classified 2484 endoscopic images as normal, Chronic Otitis Media (COM), and OM with Effusion (OME) with 94.9% accuracy using DensNet161 [8]. [PERSON] et al. trained Xception and MobileNet-V2 on 10,703 endoscopic images and classified normal, Acute Otitis Media (AOM), and OME, achieving an accuracy of 97.45% and 95.72%, respectively [9]. [PERSON] et al. used AlexNet, GoogLeNet, and DenseNet201 to extract features from 3093 temporal bone CT images. Then, they utilized a Support Vector Machine (SVM) to classify COM with cholesteatoma and without cholesteatoma with a 95.4% accuracy [10]. Thus, prior research has demonstrated good performance, with most of this based on supervised learning models. However, applying supervised learning algorithms to real-world clinical situations is difficult because of their inapplicability outside the training domain and bias due to imbalanced data [11]. To prevent this, an adequate amount of data (approximately 1000 data points per case) is necessary for all patient categories. However, privacy-related legislation limits the gathering and accessing of all types of data [12]. Creating a database that includes any type of data may result in issues, such as class imbalance, huge costs, and missing data [13]. To overcome the limitations of prior research, we propose a method that learns only normal tympanic membrane images using a VAE and calculates the distance between normal and abnormal data to detect abnormal data. Unlike in previous research, we only use normal data for learning; therefore, a large amount of data is unnecessary. In addition, the distance from the normal class is used as a classification criterion, allowing for the detection of data that cannot be collected or are difficult to diagnose with a specific condition or disease, despite being clearly abnormal. However, tympanic membrane endoscopic data may be difficult for a model to comprehend because of their high complexity. Processing high-dimensional data to facilitate model analysis is a crucial issue. A previous study decomposed high-dimensional images semantically. [PERSON] et al. estimated, with an AUC of 0.969, single RGB image 3D hand pose data [14]. The data were decomposed into independent factors, such as hand pose, shape, and color/texture, and applied to VAE. Tympanic membrane endoscopic images are also RGB images containing lots of information, such as tympanic membrane transparency and width and depth of the affected area.