Application of visual transformer in renal image analysis
Abstract Deep Self-Attention Network (Transformer) is an encoder–decoder architectural model that excels in establishing long-distance dependencies and is first applied in natural language processing. Due to its complementary nature with the inductive bias of convolutional neural network (CNN), Tran...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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BMC
2024-03-01
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| Series: | BioMedical Engineering OnLine |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12938-024-01209-z |
| _version_ | 1827326690971353088 |
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| author | Yuwei Yin Zhixian Tang Huachun Weng |
| author_facet | Yuwei Yin Zhixian Tang Huachun Weng |
| author_sort | Yuwei Yin |
| collection | DOAJ |
| description | Abstract Deep Self-Attention Network (Transformer) is an encoder–decoder architectural model that excels in establishing long-distance dependencies and is first applied in natural language processing. Due to its complementary nature with the inductive bias of convolutional neural network (CNN), Transformer has been gradually applied to medical image processing, including kidney image processing. It has become a hot research topic in recent years. To further explore new ideas and directions in the field of renal image processing, this paper outlines the characteristics of the Transformer network model and summarizes the application of the Transformer-based model in renal image segmentation, classification, detection, electronic medical records, and decision-making systems, and compared with CNN-based renal image processing algorithm, analyzing the advantages and disadvantages of this technique in renal image processing. In addition, this paper gives an outlook on the development trend of Transformer in renal image processing, which provides a valuable reference for a lot of renal image analysis. |
| first_indexed | 2024-03-07T14:49:11Z |
| format | Article |
| id | doaj.art-0013f230ee214dc4a02ded6434f32938 |
| institution | Directory Open Access Journal |
| issn | 1475-925X |
| language | English |
| last_indexed | 2024-03-07T14:49:11Z |
| publishDate | 2024-03-01 |
| publisher | BMC |
| record_format | Article |
| series | BioMedical Engineering OnLine |
| spelling | doaj.art-0013f230ee214dc4a02ded6434f329382024-03-05T19:47:38ZengBMCBioMedical Engineering OnLine1475-925X2024-03-0123112610.1186/s12938-024-01209-zApplication of visual transformer in renal image analysisYuwei Yin0Zhixian Tang1Huachun Weng2The College of Health Sciences and Engineering, University of Shanghai for Science and TechnologyThe College of Medical Technology, Shanghai University of Medicine & Health SciencesThe College of Health Sciences and Engineering, University of Shanghai for Science and TechnologyAbstract Deep Self-Attention Network (Transformer) is an encoder–decoder architectural model that excels in establishing long-distance dependencies and is first applied in natural language processing. Due to its complementary nature with the inductive bias of convolutional neural network (CNN), Transformer has been gradually applied to medical image processing, including kidney image processing. It has become a hot research topic in recent years. To further explore new ideas and directions in the field of renal image processing, this paper outlines the characteristics of the Transformer network model and summarizes the application of the Transformer-based model in renal image segmentation, classification, detection, electronic medical records, and decision-making systems, and compared with CNN-based renal image processing algorithm, analyzing the advantages and disadvantages of this technique in renal image processing. In addition, this paper gives an outlook on the development trend of Transformer in renal image processing, which provides a valuable reference for a lot of renal image analysis.https://doi.org/10.1186/s12938-024-01209-zDeep learningTransformerConvolutional neural networkAttention mechanismKidney disease |
| spellingShingle | Yuwei Yin Zhixian Tang Huachun Weng Application of visual transformer in renal image analysis BioMedical Engineering OnLine Deep learning Transformer Convolutional neural network Attention mechanism Kidney disease |
| title | Application of visual transformer in renal image analysis |
| title_full | Application of visual transformer in renal image analysis |
| title_fullStr | Application of visual transformer in renal image analysis |
| title_full_unstemmed | Application of visual transformer in renal image analysis |
| title_short | Application of visual transformer in renal image analysis |
| title_sort | application of visual transformer in renal image analysis |
| topic | Deep learning Transformer Convolutional neural network Attention mechanism Kidney disease |
| url | https://doi.org/10.1186/s12938-024-01209-z |
| work_keys_str_mv | AT yuweiyin applicationofvisualtransformerinrenalimageanalysis AT zhixiantang applicationofvisualtransformerinrenalimageanalysis AT huachunweng applicationofvisualtransformerinrenalimageanalysis |