nnFormer: Volumetric Medical Image Segmentation via a 3D Transformer
Xiamen University · Xiamen University of Technology · +3 more institutions
Abstract
Transformer, the model of choice for natural language processing, has drawn scant attention from the medical imaging community. Given the ability to exploit long-term dependencies, transformers are promising to help atypical convolutional neural networks to learn more contextualized visual representations. However, most of recently proposed transformer-based segmentation approaches simply treated transformers as assisted modules to help encode global context into convolutional representations. To address this issue, we introduce nnFormer (i.e., not-another transFormer), a 3D transformer for volumetric medical image segmentation. nnFormer not only exploits the combination of interleaved convolution and…
Citation impact
- FWCI
- 69.26
- Percentile
- 100%
- References
- 61
Authors
7- HZHong-Yu ZhouCorresponding
Xiamen University, Xiamen University of Technology, University of Hong Kong
- JGJiansen Guo
Xiamen University, Xiamen University of Technology
- YZYinghao Zhang
Xiamen University, Xiamen University of Technology
- XHXiaoguang Han
Shenzhen Research Institute of Big Data, Chinese University of Hong Kong, Shenzhen
- LYLequan Yu
University of Hong Kong
Topics & keywords
- Computer science
- Exploit
- Transformer
- Segmentation
- Artificial intelligence
- Convolutional neural network
- Image segmentation
- ENCODE
- Quality Education