The effective segmentation of esophagus and esophagus tumors from Computed Tomography (CT) images can meaningfully assist doctors in the diagnosis and treatment of esophageal cancer patients. However, problems such as the small proportion of esophageal region in CT images and the irregular shape of esophagus will make the diagnosis difficult. In practical applications, not all esophagus and esophageal cancer morphology can be included in the training set, so the generalization ability of the model is very important. These are the difficulties in segmenting the esophagus and esophageal cancer. Since some adjacent tissues and organs of the esophagus are visually close to esophagus and esophageal cancer, how to ensure that the network can extract effective distinguishing features has become the focus of research. In this paper, a novel U-Net structure ─ Channel-attention U-Net is proposed to segment esophagus and esophagus cancer from CT slices. This novel network combines a Channel Attention Module (CAM) that can distinguish esophagus and surrounding tissues by emphasizing and inhibiting channel feature and Cross-level Feature Fusion Module (CFFM) which is utilized to strengthen the generalization ability of the network by using high-level features to weight low-level features. Because the high-level features represent specific organizational information, and the low-level features represent the characteristics of detailed information such as edges and contours, the network can learn specific detailed features of a definite organization. In addition, in order to locate the esophageal region better, a 3D semi-automatic method for segmenting esophagus and esophageal cancer is proposed. The proposed network is trained using 46,400 CT pictures as the training set and divides 11,600 CT images from the dataset at a ratio of 0.2 as the validation set. Finally, 7,250 CT images were used as the test set to test the performance of the network. The experimental results show that the IoU value of our network can reach 0.625, the dice value is 0.732 and Hausdorff distance is 3.193. INDEX TERMS Esophageal cancer, channel attention mechanism, deep learning, computed tomography (CT).
The fine-grained image classification task is about differentiating between different object classes. The difficulties of the task are large intra-class variance and small inter-class variance. For this reason, improving models’ accuracies on the task heavily relies on discriminative parts’ annotations and regional parts’ annotations. Such delicate annotations’ dependency causes the restriction on models’ practicability. To tackle this issue, a saliency module based on a weakly supervised fine-grained image classification model is proposed by this article. Through our salient region localization module, the proposed model can localize essential regional parts with the use of saliency maps, while only image class annotations are provided. Besides, the bilinear attention module can improve the performance on feature extraction by using higher- and lower-level layers of the network to fuse regional features with global features. With the application of the bilinear attention architecture, we propose the different layer feature fusion module to improve the expression ability of model features. We tested and verified our model on public datasets released specifically for fine-grained image classification. The results of our test show that our proposed model can achieve close to state-of-the-art classification performance on various datasets, while only the least training data are provided. Such a result indicates that the practicality of our model is incredibly improved since fine-grained image datasets are expensive.
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