H2Former: An efficient hierarchical hybrid transformer for medical image segmentation
Accurate medical image segmentation is of great significance for computer aided diagnosis.
Although methods based on convolutional neural networks (CNNs) have achieved good …
Although methods based on convolutional neural networks (CNNs) have achieved good …
Phtrans: Parallelly aggregating global and local representations for medical image segmentation
The success of Transformer in computer vision has attracted increasing attention in the
medical imaging community. Especially for medical image segmentation, many excellent …
medical imaging community. Especially for medical image segmentation, many excellent …
Medical image segmentation via cascaded attention decoding
MM Rahman, R Marculescu - Proceedings of the IEEE/CVF …, 2023 - openaccess.thecvf.com
Transformers have shown great promise in medical image segmentation due to their ability
to capture long-range dependencies through self-attention. However, they lack the ability to …
to capture long-range dependencies through self-attention. However, they lack the ability to …
Multi-scale hierarchical vision transformer with cascaded attention decoding for medical image segmentation
MM Rahman, R Marculescu - Medical Imaging with Deep …, 2024 - proceedings.mlr.press
Transformers have shown great success in medical image segmentation. However,
transformers may exhibit a limited generalization ability due to the underlying single-scale …
transformers may exhibit a limited generalization ability due to the underlying single-scale …
HTC-Net: A hybrid CNN-transformer framework for medical image segmentation
Automated medical image segmentation is a crucial step in clinical analysis and diagnosis,
as it can improve diagnostic efficiency and accuracy. Deep convolutional neural networks …
as it can improve diagnostic efficiency and accuracy. Deep convolutional neural networks …
Pyramid medical transformer for medical image segmentation
Deep neural networks have been a prevailing technique in the field of medical image
processing. However, the most popular convolutional neural networks (CNNs) based …
processing. However, the most popular convolutional neural networks (CNNs) based …
Ds-transunet: Dual swin transformer u-net for medical image segmentation
Automatic medical image segmentation has made great progress owing to powerful deep
representation learning. Inspired by the success of self-attention mechanism in transformer …
representation learning. Inspired by the success of self-attention mechanism in transformer …
Missformer: An effective medical image segmentation transformer
X Huang, Z Deng, D Li, X Yuan - arXiv preprint arXiv:2109.07162, 2021 - arxiv.org
The CNN-based methods have achieved impressive results in medical image segmentation,
but they failed to capture the long-range dependencies due to the inherent locality of the …
but they failed to capture the long-range dependencies due to the inherent locality of the …
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 …
Guided-attention and gated-aggregation network for medical image segmentation
Recently, transformers have been widely used in medical image segmentation to capture
long-range and global dependencies using self-attention. However, they often struggle to …
long-range and global dependencies using self-attention. However, they often struggle to …