MAG-CA-Net

@article{Zheng2024,
title = {Enhancing gastrointestinal submucosal tumor recognition in endoscopic ultrasonography: A novel multi-attribute guided contextual attention network},
journal = {Expert Systems with Applications},
volume = {242},
pages = {122725},
year = {2024},
issn = {0957-4174},
doi = {https://doi.org/10.1016/j.eswa.2023.122725},
url = {https://www.sciencedirect.com/science/article/pii/S095741742303227X},
author = {Hangbin Zheng and Zhixia Dong and Tianyuan Liu and Hanyao Zheng and Xinjian Wan and Jinsong Bao},
keywords = {Knowledge-inspired, Attention mechanism, Explainable AI, Endoscopic ultrasonography, Submucosal tumor},
abstract = {Endoscopic ultrasonography (EUS) is a valuable imaging modality for diagnosing gastrointestinal submucosal tumors (SMTs). However, inherent content variations in EUS images due to gastrointestinal tract mobility and handheld ultrasound instability challenge the saliency of SMTs’ visual features. The presence of fine-grained inter-class and large intra-class differences further complicates EUS-based diagnosis. To address these issues, this paper presents a novel Multi-Attribute Guided Contextual Attention Network (MAG-CA-Net) for interpretable SMT recognition in EUS. Inspired by endoscopists’ clinical diagnosis expertise, our framework initially localizes abnormal areas based on echo attributes and subsequently determines tumor categories using contextual semantics. Experimental results demonstrate the effectiveness of MAG-CA-Net, exhibiting improved recognition recall and precision rates for gastrointestinal stromal tumor, leiomyoma, and pancreatic rest. Specifically, the MAG network facilitates abnormal area localization, while the CA mechanism guides the model to focus on the most discriminative tumor-context-associated regions. The proposed method achieved an average classification accuracy of 93.16%, an average precision of 93.17%, a weighted recall of 93.16%, and an average F1-score of 93.15 % for the three disease categories. The proposed approach provides crucial guidelines for data collection standards and model development in the clinical diagnosis process of SMTs under EUS. Its interpretability analysis enhances the credibility of clinical physicians towards assisted diagnostic methods based on deep learning. The source code will be publicly available at https://gitee.com/HangbinZheng/mag-ca-net.}
}