An Intelligent Diagnosis Method for NIID Based on Cross Self-supervision and DWI
Neuronal intranuclear inclusion disease (NIID) is a rare neurodegenerative disease primarily diagnosed through diffusion-weighted imaging (DWI). However, the limitation of human visual interpretation and clinical experience can lead to inaccuracies in diagnosis. This research proposes a deep learnin...
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| Format: | Article |
| Language: | zho |
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Science Press
2025-06-01
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| Series: | Chinese Journal of Magnetic Resonance |
| Subjects: | |
| Online Access: | http://121.43.60.238/bpxzz/article/2025/1000-4556/1000-4556-42-2-154.shtml |
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| _version_ | 1849335964559736832 |
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| author | CAO Fei XU Qianqian CHEN Hao ZU Jie LI Xiaowen TIAN Jin BAO Lei |
| author_facet | CAO Fei XU Qianqian CHEN Hao ZU Jie LI Xiaowen TIAN Jin BAO Lei |
| author_sort | CAO Fei |
| collection | DOAJ |
| description | Neuronal intranuclear inclusion disease (NIID) is a rare neurodegenerative disease primarily diagnosed through diffusion-weighted imaging (DWI). However, the limitation of human visual interpretation and clinical experience can lead to inaccuracies in diagnosis. This research proposes a deep learning method based on cross self-supervision, alongside the construction of Co-ResNet50 and CO-ViT models for intelligent auxiliary diagnosis of NIID. This method uses self-supervised learning and effectively combines the characteristics of ResNet50 and ViT networks to improve the model’s feature extraction capabilities. The experiment preprocessed 249 DWI data and divided them into 204 training sets and 45 test sets. The results reveal that the CO-ResNet50 model has the best performance, with an accuracy of 95.49%, precision of 95.51%, recall of 95.44%, F1 score of 0.954 7, and AUC of 0.989 7. These findings underscore the model's potential to provide support for clinical NIID diagnosis. |
| format | Article |
| id | doaj-art-15ae0ee47fc94dd496868a048aecc13e |
| institution | Kabale University |
| issn | 1000-4556 |
| language | zho |
| publishDate | 2025-06-01 |
| publisher | Science Press |
| record_format | Article |
| series | Chinese Journal of Magnetic Resonance |
| spelling | doaj-art-15ae0ee47fc94dd496868a048aecc13e2025-08-20T03:45:07ZzhoScience PressChinese Journal of Magnetic Resonance1000-45562025-06-0142225416310.11938/cjmr20243136An Intelligent Diagnosis Method for NIID Based on Cross Self-supervision and DWICAO Fei0XU Qianqian1CHEN Hao2ZU Jie3LI Xiaowen4TIAN Jin5BAO Lei61. The Affiliated Hospital of Xuzhou Medical University, Xuzhou 221000, China 2. School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221000, ChinaThe Affiliated Hospital of Xuzhou Medical University, Xuzhou 221000, ChinaThe Affiliated Hospital of Xuzhou Medical University, Xuzhou 221000, ChinaThe Affiliated Hospital of Xuzhou Medical University, Xuzhou 221000, ChinaThe Affiliated Hospital of Xuzhou Medical University, Xuzhou 221000, ChinaThe Affiliated Hospital of Xuzhou Medical University, Xuzhou 221000, China School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221000, ChinaNeuronal intranuclear inclusion disease (NIID) is a rare neurodegenerative disease primarily diagnosed through diffusion-weighted imaging (DWI). However, the limitation of human visual interpretation and clinical experience can lead to inaccuracies in diagnosis. This research proposes a deep learning method based on cross self-supervision, alongside the construction of Co-ResNet50 and CO-ViT models for intelligent auxiliary diagnosis of NIID. This method uses self-supervised learning and effectively combines the characteristics of ResNet50 and ViT networks to improve the model’s feature extraction capabilities. The experiment preprocessed 249 DWI data and divided them into 204 training sets and 45 test sets. The results reveal that the CO-ResNet50 model has the best performance, with an accuracy of 95.49%, precision of 95.51%, recall of 95.44%, F1 score of 0.954 7, and AUC of 0.989 7. These findings underscore the model's potential to provide support for clinical NIID diagnosis.http://121.43.60.238/bpxzz/article/2025/1000-4556/1000-4556-42-2-154.shtmlmriniidself-supervised learningintelligent diagnosisdeep learning |
| spellingShingle | CAO Fei XU Qianqian CHEN Hao ZU Jie LI Xiaowen TIAN Jin BAO Lei An Intelligent Diagnosis Method for NIID Based on Cross Self-supervision and DWI Chinese Journal of Magnetic Resonance mri niid self-supervised learning intelligent diagnosis deep learning |
| title | An Intelligent Diagnosis Method for NIID Based on Cross Self-supervision and DWI |
| title_full | An Intelligent Diagnosis Method for NIID Based on Cross Self-supervision and DWI |
| title_fullStr | An Intelligent Diagnosis Method for NIID Based on Cross Self-supervision and DWI |
| title_full_unstemmed | An Intelligent Diagnosis Method for NIID Based on Cross Self-supervision and DWI |
| title_short | An Intelligent Diagnosis Method for NIID Based on Cross Self-supervision and DWI |
| title_sort | intelligent diagnosis method for niid based on cross self supervision and dwi |
| topic | mri niid self-supervised learning intelligent diagnosis deep learning |
| url | http://121.43.60.238/bpxzz/article/2025/1000-4556/1000-4556-42-2-154.shtml |
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