Efficient Axillary Lymph Node Detection Via Two-stage Spatial-information-fusion-based CNN

• An advanced detection neural network, the improved Faster R-CNN, is presented to realize the localization and classification of axillary lymph nodes (ALNs) simultaneously, which not only classifies lymph node metastasis, but also provides doctors with the position of ALNs in the image for reference. • As ALN usually appears on the left and right sides of CECT images, an attention model, which has the ability of extracting spatial information, was added into the network, aiming to further improve the performance of the algorithm in positioning ALN. • A novel model constructed by channel fusion and bottle neck architecture is proposed and placed before the flatten operation to better fuse and transmit spatial information with less computational cost. Background and objective: Preoperative imaging diagnosis of axillary lymph node (ALN) metastasis is particularly important for breast cancer patients. This paper focuses on developing non-invasive and automatic schemes for accurate localization and classification (metastasis prediction) of ALN via contrast-enhanced computed tomography (CECT) image and deep learning models. Methods: Based on a two-stage strategy, a novel detection neural network is proposed, where the convolutional block attention module is utilized to extract spacial information and the bottleneck feature fusion module is designed for feature fusion in different scales. Results: Owing to the two embedded modules, the proposed convolutional neural network (CNN) model outperforms Faster R-CNN, YOLOv3, and EfficientDet in the sense that the achieved mAP is 0.454, higher than 0.247, 0.335, and 0.329, respectively. In particular, considering the function of classification only, the proposed model reaches the best performance on most indices (accuracy of 0.968, positive predictive value of 0.972, negative predictive value of 0.966, specificity of 0.983), compared with the methods that have been frequently adopted to predict ALN. In addition, the proposed CNN model has the function of locating ALN, which is lacking in existing models. Conclusions: In this paper, a supervised deep learning method is proposed to detect ALN in CECT images. The positive effect of new added modules are verified, and the benefits of spatial information in ALN detection are confirmed. Further, the two subtasks called localization and classification are evaluated separately, where the proposed model achieves the best performance on most indices. The source code mentioned in this article will be released later.