# Real-Time Multi-Label Upper Gastrointestinal Anatomy Recognition from Gastroscope Videos
## 4 Discussion
### Recognition Evaluation  
The purpose of this study was to use artificial intelligence to alleviate the problem that EGD quality control guidelines are not easily carried out and implemented in the clinic. In cases where the level of gastroscopy varies between physicians, there is a risk of missing a diagnosis if the entire endoscopy is not covered in that particular examination. Although similar work using CNN to assist in EGD quality control has been done in previous studies, there are several shortcomings. First, the use of a single label to represent the image is inaccurate, especially when the area occupied by different anatomical locations of the image in adjacent transition frames is large. Second, previous studies have mainly trained models on discrete still image data, which is insufficient in complex continuous video scenes and prone to a high number of false positive analysis values. In this study, we propose a new framework to address these problems. First, by introducing GCN in the training task to construct label associations, which, in turn, improves the accuracy of the location recognition. Second, the temporal associations in the video frames are addressed by introducing LSTM and continuous video frame datasets. The GCN-based model outperforms other models with a uniform backbone structure, demonstrating its effectiveness in label-dependent modeling. The GCN has more advantages in multi-label modeling compared to the natural image dataset, which also indicates a strong interdependence between gastric anatomical locations. For analogs with lower scores, such as the large curvature and posterior wall in the upper middle body, and the small curvature, anterior and posterior wall in the lower body, there was a significant improvement. This is because GCN exploits the relationship between strong and weak label features extracted by models. More importantly, we optimize the visual performance and sequential dynamics throughout the training process by introducing label associations and spatio-temporal priors. In general, the features generated by introducing more label associations and temporal feature constraints are more discriminative than those generated by traditional CNNs that consider only spatial information. In Figure 8, GL-Net can achieve accurate recognition results that conform to label association rules and correspond to image features, especially for frames with changing locations. In addition, based on LSTM, the results are more stable with fewer jumps, so the overall performance is improved, which is crucial for this task. Although there are many novel video-based 3D CNN methods proposed, we believe that compared to LSTM methods, 3D CNNs cannot provide correlations with longer connections due to the limitation of computational volume and computational speed. Therefore, we believe that using LSTM is the appropriate method for modeling temporal correlations. For the categories with relatively low scores, this may be due to the lack of distinct features and the insufficient number of datasets. However, considering the network performance, computational resources, and training difficulty, we use a 50-layer ResNet to implement GL-Net, so that the computational resources and training time can be controlled within a satisfactory range and satisfactory results can be obtained. With sufficient computational resources, we can choose a deeper CNN network to further improve the performance, or use multi-GPU distributed training. In recent years, deep learning techniques in computer vision have made rapid progress, and representative recognition network structures such as VGGNet [57], Inceptions [58], ResNet [46], DenseNet [26], MobileNet [59], EfficientNet [45], and RegNet [60] have been expanding the accuracy, effectiveness, scale, and real-time performance of the networks. The Transformer [44], a self-attentive mechanism structure extending from the field of natural language processing (NLP), has given a trend to unify and combine image and text data.