A complementary and contrastive network for stimulus segmentation and generalization

Na Ta, Haipeng Chen, Yingda Lyu, Xue Wang, Zenan Shi, Zhehao Liu

Abstract: Highlights•A novel framework CCNet is devised for accurate medical image segmentation.•Complementary features are extracted by utilizing Transformer and CNN.•We create a global context refinement module to address semantic dilution issue.•A mutual attentive module is proposed to obtain fore-/background contrastive cues.•Extensive experiments demonstrate that CCNet achieves excellent performance.