A Reconstructed UNet Model With Hybrid Fuzzy Pooling for Gastric Cancer Segmentation in Tissue Pathology Images

Utilizing artificial intelligence techniques for automated diagnosis of cancerous areas within gastric tissue pathology images can significantly augment physicians' diagnostic capabilities and subsequent treatment procedures. However, conventional segmentation models based on UNet architecture typically have images of the lesion area as outputs, failing to reconstruct the original gastric tissue pathology images from learned image features. This approach often results in incomplete learning of the complex details within gastric tissue pathology images, rendering the learned features susceptible to noise interference. Furthermore, previous segmentation models have used pooling operations, such as max, random, or average pooling, neglecting the holistic and global features present in gastric tissue pathology images, consequently failing to represent pathological features of gastric cancer tissue effectively. Therefore, we propose a reconstructed UNet model with hybrid fuzzy pooling (RUHFP) to detect lesion areas within gastric tissue pathology images. The RUHFP model is primarily based on the UNet architecture. The UNet version used in this work is U2-Net. Its novelty lies in integrating reconstruction operations from autoencoders into the UNet architecture. We jointly optimize the loss functions of both decoders to enhance the robustness of learned image features against noise interference. In addition, we incorporate fuzzy pooling operations for feature extraction, which are fused with features learned by the UNet architecture to improve the effectiveness and interpretability of image features. Several experimental tests conducted on real gastric tissue pathology image datasets validate the outstanding performance of the RUHFP model.