In Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Endoscopy is essential for polyp diagnosis and prevention of colorectal cancer. Many deep learning methods have been proposed to perform automatic semantic segmentation of polyps in endoscopic images. However, labeled training images are always scarce, and the styles of endoscopic images from different medical centers vary greatly. The annotation of medical images requires much effort, and how to make more efficient utilization of the existing labeled data is becoming an increasingly critical issue. Considering the characteristics of polyp segmentation tasks and the need for generalization, we proposed a novel method named DAN-PD based on the Vision Transformer. Moreover, we devised the Teacher Parallel Encoder (TPE) and the Domain-Aware Parallel Decoder (DAPD) for the model. Our design innovatively introduces Unsupervised Domain Adaptation (UDA) methods and adversarial learning strategies to the polyp segmentation task. We conducted four transfer learning experiments with three public polyp image datasets to examine the model's performance. The results shows that our proposed method is ahead of other methods in all experiments and reaches the state-of-the-art level.
Hu Jiaqi, Xu Yongqin, Tang Zhixian
Adversarial learning, Deep learning, Polyp segmentation, Semantic segmentation, Unsupervised domain adaptation