Mask Focal Modulation Network for Gastric Intestinal Metaplasia Segmentation

Gastric intestinal metaplasia (IM) is an important precancerous lesion with a high correlation to gastric cancer. Early gastric IM diagnosis is very important in reducing the risk of gastric cancer. In this paper, we propose the mask focal modulation network to achieve gastric IM segmentation from endoscopic images. The proposed network consists of the encoder module, pixel decoder module, and mask focal modulation decoder module. With the cooperation of the encoder module and pixel decoder module, the proposed mask focal modulation decoder module in the network aims to aggregate features of different field-of-views to make the network focus on the important views with learnable masks and then provide better gastric IM segmentation results. The experimental results show the superiority of the proposed method over state-of-the-art semantic segmentation methods in the gastric IM segmentation dataset. The source code is available at https://github.com/nchucvml/MFMNetwork.