Zheng, X., Zeng, Z., Ma, C., Chang, Q., Zhao, Z., & Yang, X. (2022). Malignancy Suspicious Region Guided Deep Neural Networks for Gastric Ulcer Classification. 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). https://doi.org/10.1109/embc48229.2022.9871776
Abstract:
Malignant transformation of gastric ulcer can result in gastric cancer, hence an accurate gastric ulcer classification method is of vital importance. Despite marvelous progress has been achieved in recent years, there are still many challenges in diagnosis of gastric ulcer. In this paper, we propose a mechanism to mimic gastroenterologist’s behaviours based on deep learning techniques, by integrating the segmented malignancy suspicious masks with gastroscopic images for gastric ulcer classification, which instructs the model to focus on the area where symptoms occur for gastric ulcer diagnosis. Specifically, a U-Net-type deep neural network is built to segment the suspicious pathological regions from gastroscopic images, then the segmented regions are treated as an attention channel of gastroscopic images for the gastric ulcer classification by a ResNet-type deep neural network. Experiments on a real gastroscopic dataset with 900+ patient cases demonstrate that our proposed approach achieves much better performance for gastric ulcer diagnosis, compared with standard method with only gastroscopic images.
License type:
Publisher Copyright
Funding Info:
There was no specific funding for the research done