# Real-Time Multi-Label Upper Gastrointestinal Anatomy Recognition from Gastroscope Videos
## 1 Introduction  
Gastric cancer [1] is the second leading cause of cancer-related deaths [2]. In clinical practice, Esophagostroduodenoscopy (EGD) is a key step in the diagnosis of upper gastrointestinal tract disease. However, the rate of misdiagnosis and underdiagnosis of gastric diseases is high, reducing the detection of precancerous lesions and gastric cancer. This is because there is a great variation in EGD performed by endoscopists with different qualifications. On one hand, some inexperienced physicians may miss some critical areas and blind corners during the examination. On the other hand, physicians in densely populated areas face long examinations every day, which may lead to missed examinations and errors due to subjective mental or physical fatigue. This may result in the endoscopist not being able to comprehensively cover all anatomical locations throughout the stomachduring the examination. Studies have shown that high-quality endoscopy can lead to more accurate diagnostic results [3], and it is crucial to further expand endoscopic techniques and improve routine endoscopy coverage and examination quality. Many authorities have now proposed clinical examination guidelines with corresponding expert consensus to evaluate and optimize the quality of endoscopy. The American Society of Gastrointestinal Endoscopy (ASGE) and the American College of Gastroenterology (ACG) have developed and published quality metrics common to all endoscopic procedures in EGD. The European Society of Gastroenterology (ESGE) systematically surveyed the available evidence and developed the first evidence-based performance measures for EGD (procedural integrity, examination time, etc.) in 2015 [4; 5]. However, the lack of practical tools for rigorous monitoring and evaluation makes it difficult to apply many quantitative quality control indicators [6] (e.g., whether comprehensive coverage of anatomical location examination is achieved) in practice, which is a major constraint to quality control efforts. The quality standard of GI endoscopy can be defined as: when doctors do endoscopy, they need to ensure that all key parts of the GI tract are within the scope of the examination, and maintain an appropriate observation duration, leaving no blind spots and avoiding the lens moving too fast or missing the observation of key areas. In recent years, advances in deep learning-based artificial intelligence technologies have continued to soar, with significant progress in the field of medical image recognition. Quality control of gastrointestinal endoscopy is the basis for the application of AI technology in endoscopic imaging and the prerequisite for applying artificial intelligence technology to disease screening and supplementary diagnosis. Advances have been made in the identification of gastric diseases [7; 8], precancerous lesions [9; 10; 11; 12; 13; 14] and gastric cancer [15; 16; 17; 18; 19; 20; 21]. It is important to use artificial intelligence systems to monitor the indicators in the quality control of gastrointestinal endoscopy in real time. However, previous studies have mainly focused on the intelligent auxiliary diagnosis of GI lesions. Due to the lack of relevant datasets for anatomical structures and the more complex and large data annotation efforts for this type of task, only a few studies were devoted to quality monitoring of routine endoscopy. [PERSON] et al. [22] divided the anatomical location of the stomach into 10 and subdivided it into 26. DCNN was applied for anatomy classification. The final accuracy rates were 90% and 65.9%, respectively. Based on DCNN and reinforcement learning, 26 gastric anatomical locations were classified [23], and blind spots in EGD videos were monitored with an accuracy of 90.02%, which served to monitor the quality of real-time examinations. [PERSON] et al. [24] proposed a deep ensemble feature network to combine the features extracted by multiple CNNs, to boost the recognition of three anatomic sites and two image modals with an accuracy of 96.9% and 23.8 frames per second(FPS).