Transformers in medical imaging: A survey
Following unprecedented success on the natural language tasks, Transformers have been
successfully applied to several computer vision problems, achieving state-of-the-art results …
successfully applied to several computer vision problems, achieving state-of-the-art results …
[Retracted] U‐Net‐Based Medical Image Segmentation
Deep learning has been extensively applied to segmentation in medical imaging. U‐Net
proposed in 2015 shows the advantages of accurate segmentation of small targets and its …
proposed in 2015 shows the advantages of accurate segmentation of small targets and its …
Self-supervised pre-training of swin transformers for 3d medical image analysis
Abstract Vision Transformers (ViT) s have shown great performance in self-supervised
learning of global and local representations that can be transferred to downstream …
learning of global and local representations that can be transferred to downstream …
An effective CNN and Transformer complementary network for medical image segmentation
F Yuan, Z Zhang, Z Fang - Pattern Recognition, 2023 - Elsevier
The Transformer network was originally proposed for natural language processing. Due to
its powerful representation ability for long-range dependency, it has been extended for …
its powerful representation ability for long-range dependency, it has been extended for …
Swin transformer embedding UNet for remote sensing image semantic segmentation
X He, Y Zhou, J Zhao, D Zhang… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Global context information is essential for the semantic segmentation of remote sensing (RS)
images. However, most existing methods rely on a convolutional neural network (CNN) …
images. However, most existing methods rely on a convolutional neural network (CNN) …
nnformer: Volumetric medical image segmentation via a 3d transformer
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 …
from the medical imaging community. Given the ability to exploit long-term dependencies …
[HTML][HTML] Transformers in medical image analysis
Transformers have dominated the field of natural language processing and have recently
made an impact in the area of computer vision. In the field of medical image analysis …
made an impact in the area of computer vision. In the field of medical image analysis …
nnformer: Interleaved transformer for volumetric segmentation
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 …
from the medical imaging community. Given the ability to exploit long-term dependencies …
Transforming medical imaging with Transformers? A comparative review of key properties, current progresses, and future perspectives
Transformer, one of the latest technological advances of deep learning, has gained
prevalence in natural language processing or computer vision. Since medical imaging bear …
prevalence in natural language processing or computer vision. Since medical imaging bear …
Hiformer: Hierarchical multi-scale representations using transformers for medical image segmentation
Convolutional neural networks (CNNs) have been the consensus for medical image
segmentation tasks. However, they inevitably suffer from the limitation in modeling long …
segmentation tasks. However, they inevitably suffer from the limitation in modeling long …