Multi-domain integrative Swin transformer network for sparse-view tomographic reconstruction

Pan, Jiayi; Zhang, Heye; Wu, Weifei; Gao, Zhifan; Wu, Weiwen

doi:10.1016/j.patter.2022.100498

Cited by 45 publications

(19 citation statements)

References 45 publications

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“…developed a multi-domain integrative Swin transformer network (MIST-net) for sparse-view reconstruction. 56 Furthermore, the Swin transformer was used for MRI reconstruction. 57 How to stabilize transformer-based deep reconstruction networks is also important.…”

Section: Discussionmentioning

confidence: 99%

Stabilizing deep tomographic reconstruction: Part A. Hybrid framework and experimental results

Cong

et al. 2022

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Section: Discussionmentioning

confidence: 99%

Stabilizing deep tomographic reconstruction: Part A. Hybrid framework and experimental results

Cong

et al. 2022

Patterns

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“…Therefore, the transformer [ 28 , 29 ] is widely used in the field of image processing because of its ability to better access information and integrate the CNN and transformer. Pan et al [ 23 ] proposed a high-quality reconstruction transformer to capture image global features for medical CT image reconstruction The use of SR in the field of medical CT imaging: DL technology is extensively employed for medical CT imaging [ 30 – 32 ]. Many scholars have applied SR technology to the medical field [ 33 – 35 ].…”

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

“…However, CNNs cannot realize long-range feature extraction. The transformer is mostly employed in the audio industry, but it has been, recently, employed for SR as a replacement for CNNs [ 23 , 24 ] because the transformer can support long-range feature extraction by using a self-attention (SA) mechanism and yields very good performance in the image domain. Transformer models in the field of medical imaging have been extensively studied [ 25 ].…”

Section: Introductionmentioning

confidence: 99%

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Super‐Resolution Swin Transformer and Attention Network for Medical CT Imaging

Zheng

Wang

et al. 2022

BioMed Research International

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Computerized tomography (CT) is widely used for clinical screening and treatment planning. In this study, we aimed to reduce X-ray radiation and achieve high-quality CT imaging by using low-intensity X-rays because CT radiation is damaging to the human body. An innovative vision transformer for medical image super-resolution (SR) is applied to establish a high-definition image target. To achieve this, we proposed a method called swin transformer and attention network (STAN) that uses the swin transformer network, which employs an attention method to overcome the long-range dependency difficulties encountered in CNNs and RNNs to enhance and restore the quality of medical CT images. We adopted the peak signal-to-noise ratio for performance comparison with other mainstream SR reconstruction models used in medical CT imaging. Experimental results revealed that the proposed STAN model yields superior medical CT imaging results than the existing SR techniques based on CNNs. The proposed STAN model employs a self-attention mechanism to more effectively extract critical features and long-range information, hence enhancing the quality of medical CT image reconstruction.

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“…For the first time, Vision Transformer (ViT) divides an image into a sequence of non-overlap patches, analyzes them as a sequence of elements similar to words, and produces state-of-the-art results demonstrating the effectiveness and superiority in image classification (Dosovitskiy et al;. Since then, ViT has been successfully applied to various other vision tasks including medical imaging Pan et al (2021) and medical image analysis (Lyu et al;. However, the performance of the original ViT relies on a large labelled image dataset including 300 millions images, and the conventional wisdom is that the transformers do not generalize well if they are trained on insufficient amounts of data.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Contrastive Learning based Transformer for Lung Nodule Detection

Niu¹,

Wang²

2022

Preprint

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Early detection of lung nodules with computed tomography (CT) is critical for the longer survival of lung cancer patients and better quality of life. Computer-aided detection/diagnosis (CAD) is proven valuable as a second or concurrent reader in this context. However, accurate detection of lung nodules remains a challenge for such CAD systems and even radiologists due to not only the variability in size, location, and appearance of lung nodules but also the complexity of lung structures. This leads to a high false-positive rate with CAD, compromising its clinical efficacy. Motivated by recent computer vision techniques, here we present a self-supervised region-based 3D transformer model to identify lung nodules among a set of candidate regions. Specifically, a 3D vision transformer (ViT) is developed that divides a CT image volume into a sequence of non-overlap cubes, extracts embedding features from each cube with an embedding layer, and analyzes all embedding features with a self-attention mechanism for the prediction. To effectively train the transformer model on a relatively small dataset, the region-based contrastive learning method is used to boost the performance by pre-training the 3D transformer with public CT images. Our experiments show that the proposed method can significantly improve the performance of lung nodule screening in comparison with the commonly used 3D convolutional neural networks.

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Multi-domain integrative Swin transformer network for sparse-view tomographic reconstruction

Cited by 45 publications

References 45 publications

Stabilizing deep tomographic reconstruction: Part A. Hybrid framework and experimental results

Stabilizing deep tomographic reconstruction: Part A. Hybrid framework and experimental results

Super‐Resolution Swin Transformer and Attention Network for Medical CT Imaging

Unsupervised Contrastive Learning based Transformer for Lung Nodule Detection

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