2021

CoTr: Efficiently Bridging CNN and Transformer for 3D Medical Image Segmentation

Xie, Yutong, Zhang, Jianpeng, Shen, Chunhua et al.

Understand

Convolutional neural networks (CNNs) have been the de facto standard for nowadays 3D medical image segmentation.

The convolutional operations used in these networks, however, inevitably have limitations in modeling the long-range dependency due to their inductive bias of locality and weight sharing.
Although Transformer was born to address this issue, it suffers from extreme computational and spatial complexities in processing high-resolution 3D feature maps.
In this paper, we propose a novel framework that efficiently bridges a {\bf Co}nvolutional neural network and a {\bf Tr}ansformer {\bf (CoTr)} for accurate 3D medical image segmentation.

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