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 …
Brain tumor segmentation based on the fusion of deep semantics and edge information in multimodal MRI
Brain tumor segmentation in multimodal MRI has great significance in clinical diagnosis and
treatment. The utilization of multimodal information plays a crucial role in brain tumor …
treatment. The utilization of multimodal information plays a crucial role in brain tumor …
Medical sam adapter: Adapting segment anything model for medical image segmentation
The Segment Anything Model (SAM) has recently gained popularity in the field of image
segmentation due to its impressive capabilities in various segmentation tasks and its prompt …
segmentation due to its impressive capabilities in various segmentation tasks and its prompt …
Unext: Mlp-based rapid medical image segmentation network
JMJ Valanarasu, VM Patel - … conference on medical image computing and …, 2022 - Springer
UNet and its latest extensions like TransUNet have been the leading medical image
segmentation methods in recent years. However, these networks cannot be effectively …
segmentation methods in recent years. However, these networks cannot be effectively …
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 …
Image segmentation using text and image prompts
T Lüddecke, A Ecker - … of the IEEE/CVF conference on …, 2022 - openaccess.thecvf.com
Image segmentation is usually addressed by training a model for a fixed set of object
classes. Incorporating additional classes or more complex queries later is expensive as it …
classes. Incorporating additional classes or more complex queries later is expensive as it …
Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images
Semantic segmentation of brain tumors is a fundamental medical image analysis task
involving multiple MRI imaging modalities that can assist clinicians in diagnosing the patient …
involving multiple MRI imaging modalities that can assist clinicians in diagnosing the patient …
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) …