CCAT-NET: A Novel Transformer Based Semi-Supervised Framework For Covid-19 Lung Lesion Segmentation

Liu, Mingyang; Xiao, Li; Jiang, Huiqin

doi:10.1109/isbi52829.2022.9761533

Cited by 13 publications

(8 citation statements)

References 20 publications

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“…Considering the serious damage of COVID-19 on global health and economy, it is urgent to achieve a fast and effective diagnosis of COVID-19. Thus, authors in Refs, 75,[90][91][92][93][94][95][96][122][123][124][125][127][128][129]133,135,137 explored to achieve a rapid and accurate COVID-19 classification using Transformerbased architectures. Shome et al 93 proposed a Covid-Transformer to achieve the automatic examining of COVID-19 according to the X-ray image.…”

Section: Covid-19 Analysismentioning

confidence: 99%

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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Section: Covid-19 Analysismentioning

confidence: 99%

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

Xia

Wang

2023

MedComm – Future Medicine

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“…As different models usually extract different representations, the different models in co-training framework can focus on different views. Except using CNN as the backbones, there are also some transformer-based backbones [77], [78]. As shown in Figure 9, Luo et al [79] introduce the cross teaching between CNN and Transformer which implicitly encourages the consistency and complementary between different networks.…”

Section: Unsupervised Regularization With Co-trainingmentioning

confidence: 99%

“…As shown in Figure 9, Luo et al [79] introduce the cross teaching between CNN and Transformer which implicitly encourages the consistency and complementary between different networks. Liu et al [77] combine CNN blocks and Swin Transformer blocks as the backbone. Xiao et al [78] add another teacher model with the transformer-based architecture.…”

Section: Unsupervised Regularization With Co-trainingmentioning

confidence: 99%

Learning with Limited Annotations: A Survey on Deep Semi-Supervised Learning for Medical Image Segmentation

Jiao¹,

Zhang²,

Liu³

et al. 2022

Preprint

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Medical image segmentation is a fundamental and critical step in many image-guided clinical approaches. Recent success of deep learning-based segmentation methods usually relies on a large amount of labeled data, which is particularly difficult and costly to obtain especially in the medical imaging domain where only experts can provide reliable and accurate annotations. Semi-supervised learning has emerged as an appealing strategy and been widely applied to medical image segmentation tasks to train deep models with limited annotations. In this paper, we present a comprehensive review of recently proposed semi-supervised learning methods for medical image segmentation and summarized both the technical novelties and empirical results. Furthermore, we analyze and discuss the limitations and several unsolved problems of existing approaches. We hope this review could inspire the research community to explore solutions for this challenge and further promote the developments in medical image segmentation field.

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“…Skip connection is implemented at different scales. Liu et al [149] developed a CCAT-net to segment CT images [38]. Both encoder and decoder in the proposed network are composed of three CNN blocks and three swin transformer blocks.…”

Section: Segmentationmentioning

confidence: 99%

Recent Progress in Transformer-based Medical Image Analysis

Liu¹,

Shen²

2022

Preprint

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The transformer has dominated the natural language processing (NLP) field for a long time. Recently, the transformer-based method has been adopted into the computer vision (CV) field and shows promising results. As an important branch of the CV field, medical image analysis joins the wave of the transformer-based method rightfully. In this review, we illustrate the principle of the attention mechanism, and the detailed structures of the transformer, and depict how the transformer is adopted into medical image analysis. We organize the transformer-based medical image analysis applications in a sequence of different tasks, including classification, segmentation, synthesis, registration, localization, detection, captioning, and denoising. For the mainstream classification and segmentation tasks, we further divided the corresponding works based on different medical imaging modalities. The datasets corresponding to the related works are also organized. We include thirteen modalities and more than twenty objects in our work.

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CCAT-NET: A Novel Transformer Based Semi-Supervised Framework For Covid-19 Lung Lesion Segmentation

Cited by 13 publications

References 20 publications

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

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

Learning with Limited Annotations: A Survey on Deep Semi-Supervised Learning for Medical Image Segmentation

Recent Progress in Transformer-based Medical Image Analysis

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