{"type":"ScholarlyArticle","title":"CiT-Net: Convolutional Neural Networks Hand in Hand with Vision Transformers for Medical Image Segmentation","authors":[{"name":"Tao Lei","slug":"tao-lei","given":"Tao","family":"Lei","affiliation":"Shaanxi Joint Laboratory of Artificial Intelligence , Shaanxi University of Science and Technology; Department of Geriatric Surgery , First Affiliated Hospital , Xi'an Jiaotong University"},{"name":"Rui Sun","slug":"rui-sun","given":"Rui","family":"Sun","affiliation":"Shaanxi Joint Laboratory of Artificial Intelligence , Shaanxi University of Science and Technology"},{"name":"Xuan Wang","slug":"xuan-wang","given":"Xuan","family":"Wang","affiliation":"Unmanned System Research Institute , Northwestern Polytechnical University"},{"name":"Yingbo Wang","slug":"yingbo-wang","given":"Yingbo","family":"Wang","affiliation":"Shaanxi Joint Laboratory of Artificial Intelligence , Shaanxi University of Science and Technology"},{"name":"Xi He","slug":"xi-he","given":"Xi","family":"He","affiliation":"Shaanxi Joint Laboratory of Artificial Intelligence , Shaanxi University of Science and Technology"},{"name":"Asoke Nandi","slug":"asoke-nandi","given":"Asoke","family":"Nandi","affiliation":"Department of Electronic and Electrical Engineering , Brunel University London"}],"abstract":"The hybrid architecture of convolutional neural networks (CNNs) and Transformer are very popular for medical image segmentation. However, it suffers from two challenges. First, although a CNNs branch can capture the local image features using vanilla convolution, it cannot achieve adaptive feature learning. Second, although a Transformer branch can capture the global features, it ignores the channel and cross-dimensional self-attention, resulting in a low segmentation accuracy on complex-content images. To address these challenges, we propose a novel hybrid architecture of convolutional neural networks hand in hand with vision Transformers (CiT-Net) for medical image segmentation. Our network has two advantages. First, we design a dynamic deformable convolution and apply it to the CNNs branch, which overcomes the weak feature extraction ability due to fixed-size convolution kernels and the stiff design of sharing kernel parameters among different inputs. Second, we design a shifted-window adaptive complementary attention module and a compact convolutional projection. We apply them to the Transformer branch to learn the cross-dimensional long-term dependency for medical images. Experimental results show that our CiT-Net provides better medical image segmentation results than popular SOTA methods. Besides, our CiT-Net requires lower parameters and less computational costs and does not rely on pre-training. The code is publicly available at https://github.com/SR0920/CiT-Net.","doi":"10.24963/ijcai.2023/113","url":"https://ijcai.org/proceedings/2023/113","pdf":"https://ijcai.org/proceedings/2023/0113.pdf","markdown":"https://ijcai.org/proceedings/2023/113.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/113","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Computer Vision","track":"Main Track","number":113,"pages":{"first":1017,"last":1025},"keywords":["Computer Vision: CV: Biomedical image analysis","Computer Vision: CV: Segmentation","Computer Vision: CV: Machine learning for vision"],"videos":[],"references":[{"position":1,"raw":"[Alom et al., 2018] Md Zahangir Alom, Mahmudul Hasan, Chris Yakopcic, Tarek M Taha, and Vijayan K Asari. 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