CiT-Net: Convolutional Neural Networks Hand in Hand with Vision Transformers for Medical Image Segmentation

• URL: http://arxiv.org/abs/2306.03373v2
• Date: Wed, 20 Dec 2023 02:42:13 GMT
• Title: CiT-Net: Convolutional Neural Networks Hand in Hand with Vision Transformers for Medical Image Segmentation
• Authors: Tao Lei, Rui Sun, Xuan Wang, Yingbo Wang, Xi He, Asoke Nandi
• Abstract summary: We propose a novel hybrid architecture of convolutional neural networks (CNNs) and vision Transformers (CiT-Net) for medical image segmentation. Our CiT-Net provides better medical image segmentation results than popular SOTA methods.
• Score: 10.20771849219059
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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