High-Resolution Swin Transformer for Automatic Medical Image Segmentation

Chen, Wei; Ren, Shenghan; Guo, Kaitai; Hu, Haihong; Liang, Jimin

doi:10.3390/s23073420

Cited by 26 publications

(8 citation statements)

References 38 publications

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“…Refs. 14,16,27,30 integrated Transformer with other existing efficient frameworks. Specifically, Wei et al 14 proposed a HRSTNet which combined the HRNet and Swin Transformer.…”

Section: Other Methodsmentioning

confidence: 99%

“…14,16,27,30 integrated Transformer with other existing efficient frameworks. Specifically, Wei et al 14 proposed a HRSTNet which combined the HRNet and Swin Transformer. Li et al 16 adopted the structure of FC-DenseNet joined with ResLinear-Transformer (RL-Transformer) and convolutional linear attention block (CLAB) and proposed the TFCNs.…”

Section: Other Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Recent advances of Transformers in medical image analysis: A comprehensive review

Xia

Wang

2023

MedComm – Future Medicine

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Recent works have shown that Transformer's excellent performances on natural language processing tasks can be maintained on natural image analysis tasks. However, the complicated clinical settings in medical image analysis and varied disease properties bring new challenges for the use of Transformer. The computer vision and medical engineering communities have devoted significant effort to medical image analysis research based on Transformer with especial focus on scenario-specific architectural variations.In this paper, we comprehensively review this rapidly developing area by covering the latest advances of Transformer-based methods in medical image analysis of different settings. We first give introduction of basic mechanisms of Transformer including implementations of selfattention and typical architectures. The important research problems in various medical image data modalities, clinical visual tasks, organs and diseases are then reviewed systemically. We carefully collect 276 very recent works and 76 public medical image analysis datasets in an organized structure. Finally, discussions on open problems and future research directions are also provided. We expect this review to be an up-to-date roadmap and serve as a reference source in pursuit of boosting the development of medical image analysis field.

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“…Refs. 14,16,27,30 integrated Transformer with other existing efficient frameworks. Specifically, Wei et al 14 proposed a HRSTNet which combined the HRNet and Swin Transformer.…”

Section: Other Methodsmentioning

confidence: 99%

Section: Other Methodsmentioning

confidence: 99%

Recent advances of Transformers in medical image analysis: A comprehensive review

Xia

Wang

2023

MedComm – Future Medicine

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“…In recent years, due to the great achievements of deep learning in the field of medical image segmentation [ 21 , 22 , 23 ], researchers began to explore semi-automatic annotation based on deep learning [ 24 , 25 , 26 , 27 , 28 , 29 ]. Pair [ 30 ] is a semi-automatic annotation tool based on deep learning developed by Shenzhen University for medical image annotation tasks.…”

Section: Introductionmentioning

confidence: 99%

A Semi-Automatic Magnetic Resonance Imaging Annotation Algorithm Based on Semi-Weakly Supervised Learning

Chen,

Zhang

2024

Sensors

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The annotation of magnetic resonance imaging (MRI) images plays an important role in deep learning-based MRI segmentation tasks. Semi-automatic annotation algorithms are helpful for improving the efficiency and reducing the difficulty of MRI image annotation. However, the existing semi-automatic annotation algorithms based on deep learning have poor pre-annotation performance in the case of insufficient segmentation labels. In this paper, we propose a semi-automatic MRI annotation algorithm based on semi-weakly supervised learning. In order to achieve a better pre-annotation performance in the case of insufficient segmentation labels, semi-supervised and weakly supervised learning were introduced, and a semi-weakly supervised learning segmentation algorithm based on sparse labels was proposed. In addition, in order to improve the contribution rate of a single segmentation label to the performance of the pre-annotation model, an iterative annotation strategy based on active learning was designed. The experimental results on public MRI datasets show that the proposed algorithm achieved an equivalent pre-annotation performance when the number of segmentation labels was much less than that of the fully supervised learning algorithm, which proves the effectiveness of the proposed algorithm.

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“…Notably, Swin Transformer (SwinT) offers scalability, allowing it to process images of different sizes and adapt to diverse datasets and resolutions while maintaining competitive performance, reducing computational demands [ 21 ]. Impressively, SwinT approaches exhibit robust generalization capabilities across a spectrum of computer vision tasks with minimal task-specific adjustments, and their interpretability and mitigation of overfitting contribute to their appeal [ 22 , 23 ].…”

Section: Introductionmentioning

confidence: 99%

Classification of Muscular Dystrophies from MR Images Improves Using the Swin Transformer Deep Learning Model

Mastropietro,

Casali,

Taccogna

et al. 2024

Bioengineering

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Muscular dystrophies present diagnostic challenges, requiring accurate classification for effective diagnosis and treatment. This study investigates the efficacy of deep learning methodologies in classifying these disorders using skeletal muscle MRI scans. Specifically, we assess the performance of the Swin Transformer (SwinT) architecture against traditional convolutional neural networks (CNNs) in distinguishing between healthy individuals, Becker muscular dystrophy (BMD), and limb–girdle muscular Dystrophy type 2 (LGMD2) patients. Moreover, 3T MRI scans from a retrospective dataset of 75 scans (from 54 subjects) were utilized, with multiparametric protocols capturing various MRI contrasts, including T1-weighted and Dixon sequences. The dataset included 17 scans from healthy volunteers, 27 from BMD patients, and 31 from LGMD2 patients. SwinT and CNNs were trained and validated using a subset of the dataset, with the performance evaluated based on accuracy and F-score. Results indicate the superior accuracy of SwinT (0.96), particularly when employing fat fraction (FF) images as input; it served as a valuable parameter for enhancing classification accuracy. Despite limitations, including a modest cohort size, this study provides valuable insights into the application of AI-driven approaches for precise neuromuscular disorder classification, with potential implications for improving patient care.

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High-Resolution Swin Transformer for Automatic Medical Image Segmentation

Cited by 26 publications

References 38 publications

Recent advances of Transformers in medical image analysis: A comprehensive review

Recent advances of Transformers in medical image analysis: A comprehensive review

A Semi-Automatic Magnetic Resonance Imaging Annotation Algorithm Based on Semi-Weakly Supervised Learning

Classification of Muscular Dystrophies from MR Images Improves Using the Swin Transformer Deep Learning Model

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