Transformers in medical imaging: A survey

F Shamshad, S Khan, SW Zamir, MH Khan… - Medical Image …, 2023 - Elsevier
Following unprecedented success on the natural language tasks, Transformers have been
successfully applied to several computer vision problems, achieving state-of-the-art results …

[HTML][HTML] Transformers in medical image analysis

K He, C Gan, Z Li, I Rekik, Z Yin, W Ji, Y Gao, Q Wang… - Intelligent …, 2023 - Elsevier
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 …

Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer

H Wang, P Cao, J Wang, OR Zaiane - Proceedings of the AAAI …, 2022 - ojs.aaai.org
Most recent semantic segmentation methods adopt a U-Net framework with an encoder-
decoder architecture. It is still challenging for U-Net with a simple skip connection scheme to …

Transformers in medical image segmentation: A review

H Xiao, L Li, Q Liu, X Zhu, Q Zhang - Biomedical Signal Processing and …, 2023 - Elsevier
Abstract Background and Objectives: Transformer is a model relying entirely on self-
attention which has a wide range of applications in the field of natural language processing …

Ds-transunet: Dual swin transformer u-net for medical image segmentation

A Lin, B Chen, J Xu, Z Zhang, G Lu… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Automatic medical image segmentation has made great progress owing to powerful deep
representation learning. Inspired by the success of self-attention mechanism in transformer …

Transforming medical imaging with Transformers? A comparative review of key properties, current progresses, and future perspectives

J Li, J Chen, Y Tang, C Wang, BA Landman… - Medical image …, 2023 - Elsevier
Transformer, one of the latest technological advances of deep learning, has gained
prevalence in natural language processing or computer vision. Since medical imaging bear …

[HTML][HTML] Deep learning attention mechanism in medical image analysis: Basics and beyonds

X Li, M Li, P Yan, G Li, Y Jiang, H Luo… - International Journal of …, 2023 - sciltp.com
With the improvement of hardware computing power and the development of deep learning
algorithms, a revolution of" artificial intelligence (AI)+ medical image" is taking place …

Transattunet: Multi-level attention-guided u-net with transformer for medical image segmentation

B Chen, Y Liu, Z Zhang, G Lu… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Accurate segmentation of organs or lesions from medical images is crucial for reliable
diagnosis of diseases and organ morphometry. In recent years, convolutional encoder …

RSSFormer: Foreground saliency enhancement for remote sensing land-cover segmentation

R Xu, C Wang, J Zhang, S Xu, W Meng… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
High spatial resolution (HSR) remote sensing images contain complex foreground-
background relationships, which makes the remote sensing land cover segmentation a …

A survey on deep learning for skin lesion segmentation

Z Mirikharaji, K Abhishek, A Bissoto, C Barata… - Medical Image …, 2023 - Elsevier
Skin cancer is a major public health problem that could benefit from computer-aided
diagnosis to reduce the burden of this common disease. Skin lesion segmentation from …