SwinT-Unet: Hybrid architecture for Medical Image Segmentation Based on Swin transformer block and Dual-Scale Information

Atek, Sara; Mehidi, Imane; Jabri, Dalel; Belkhiat, Djamel Eddine Chouaib

doi:10.1109/ispa54004.2022.9786367

Cited by 11 publications

(4 citation statements)

References 19 publications

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“…The standard transformer performs global self-attention in images, whose computation is quadratic in complexity to the input vector and is not suitable for high-resolution images, especially for large computational tasks, such as remote sensing data processing 27 – 33 Swin Transformer proposes to perform self-attention in nonoverlapping windows. Computational complexity of a global MSA module and a window based one W-MSA is Ω(MSA)=4 hωC2+2(hωfalse)2C,Ω(W−MSA)=4 hωC2+2M2hωC,where h,ω, and C are the length, width, and number of channels of feature map, respectively, and M is the size of window.…”

Section: Methodsmentioning

confidence: 99%

MST-UNet: a modified Swin Transformer for water bodies’ mapping using Sentinel-2 images

Xie

2023

J. Appl. Rem. Sens.

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Deep learning is widely used in remote sensing field of feature recognition.Symmetric encoder-decoder network, such as UNet, is one of the most commonly used image segmentation networks, but the accuracy is often low due to its simple structure. We combine two neural network models of convolutional neural network (CNN) and Swin Transformer called modified Swin Transformer using UNet structure (MST-UNet) to achieve accurate segmentation of water bodies from remote sensing data, with Xiamen City as study area. MST-UNet is based on symmetric encoder-decoder network. We use CNN and Swin Transformer blocks to extract features from input images and capture the interdependence among different pixels, respectively. More attention is paid to global information of images. By four times upsampling to obtain predictions, the results show that the accuracy of MST-UNet is better than UNet and its improved models. The Intersection of Union (IoU), mean IoU, and Dice score on test set reach 87.80%, 92.93%, 93.08%, respectively, which verifies the feasibility of the MST-UNet. This experiment has a reference value for related studies.

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Section: Methodsmentioning

confidence: 99%

MST-UNet: a modified Swin Transformer for water bodies’ mapping using Sentinel-2 images

Xie

2023

J. Appl. Rem. Sens.

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“…Furthermore, a data-efficient image transformer (DeiT) is presented in [23], indicating that Transformer can be trained on midsize datasets, and a more robust Transformer can be obtained by combining it with the distillation method. In [24], a hierarchical Swin Transformer is developed. The authors of [24] achieved state-of-the-art performance on image classification, object detection, and semantic segmentation by considering Swin Transformer as a vision backbone.…”

Section: Introductionmentioning

confidence: 99%

“…In [24], a hierarchical Swin Transformer is developed. The authors of [24] achieved state-of-the-art performance on image classification, object detection, and semantic segmentation by considering Swin Transformer as a vision backbone. The success of ViT, DeiT, and Swin Transformer in image recognition tasks demonstrates the potential for Transformer to be applied in the vision domain.…”

Section: Introductionmentioning

confidence: 99%

“…The success of ViT, DeiT, and Swin Transformer in image recognition tasks demonstrates the potential for Transformer to be applied in the vision domain. To eliminate the weaknesses, we study Swin Transformer to obtain better results from Swin-Unet [24]. Specifically, hierarchical Swin Transformer [25] with shifted windows is used as the encoder to extract irregular structural building features for hyperspectral image segmentation.…”

Section: Introductionmentioning

confidence: 99%

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Damaged Building Detection with Improved Swin-Unet Model

Cao

et al. 2022

Wireless Communications and Mobile Computing

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Automatic detection of damaged buildings from satellite remote sensing data has become an urgent problem to rescue planners and military personnel. Unfortunately damaged buildings are in different dimensions and shapes with different roofs depending on the type of the material to be painted. In this study, we present an improved Swin-Unet approach that comprises three main operations. First, improved Swin-Unet as a Unet-like pure Transformer is used for multitemporal image segmentation. Second, different multitemporal features are extracted using hyperspectral image classification algorithm. Finally, a binary change map is generated, and evaluation results are obtained. This article takes AIST building change detection scene as the example, and compared with the conventional approaches tested, overall accuracy, mean intersection over union, and separated Kappa in the proposed method were improved by at least 23.36, 0.1725, and 0.202, respectively. Furthermore, different scenes, such as Gaofen-2/Jilin-1 multitemporal optical images and satellite imagery dataset (xBD), have also come to the same conclusion. Thus, it provides advantageous capabilities for monitoring damaged buildings along coastal areas.

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STU3: Multi-organ CT Medical Image Segmentation Model Based on Transformer and UNet

Zheng,

Li,

Chen

2024

Lecture Notes in Computer Science

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SwinT-Unet: Hybrid architecture for Medical Image Segmentation Based on Swin transformer block and Dual-Scale Information

Cited by 11 publications

References 19 publications

MST-UNet: a modified Swin Transformer for water bodies’ mapping using Sentinel-2 images

MST-UNet: a modified Swin Transformer for water bodies’ mapping using Sentinel-2 images

Damaged Building Detection with Improved Swin-Unet Model

STU3: Multi-organ CT Medical Image Segmentation Model Based on Transformer and UNet

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