Nearest-Neighbor Dual-Path Contrastive Learning for Lumbar Disc Herniation MRI Image Classification
Lumbar disc herniation (LDH) is a common spinal condition that profoundly affects patients’ quality of life. Timely and precise diagnosis is essential for efficient therapy and enhancing patient outcomes. This paper introduces an innovative LDH classification framework utilizing nearest-n...
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| Format: | Article |
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IEEE
2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/11006703/ |
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| author | Dan Pan Yu-Xiang Pan Hui Wang Qi-Jing Liu Chang-Mao Qiu |
| author_facet | Dan Pan Yu-Xiang Pan Hui Wang Qi-Jing Liu Chang-Mao Qiu |
| author_sort | Dan Pan |
| collection | DOAJ |
| description | Lumbar disc herniation (LDH) is a common spinal condition that profoundly affects patients’ quality of life. Timely and precise diagnosis is essential for efficient therapy and enhancing patient outcomes. This paper introduces an innovative LDH classification framework utilizing nearest-neighbor dual-path contrastive learning, integrating global and local feature learning to improve lumbar MRI image classification efficacy. The global path delineates semantic linkages among samples by nearest-neighbor contrastive learning, enhancing global representations, whereas the local path employs clipped regions and data augmentation to highlight essential details, thus improving fine-grained feature modeling. The novel nearest-neighbor-based positive sample construction enhances feature consistency and classification accuracy by reducing the impact of irrelevant examples. Our method achieves state-of-the-art accuracy and robustness in complicated classification tasks, as shown by experimental results on the lumbar MRI dataset. This discovery enhances automated LDH diagnosis and offers a viable avenue for accurate and efficient automated diagnosis in intricate medical imaging contexts. |
| format | Article |
| id | doaj-art-8dfcb10dd1f74775a3ea03e927c1b923 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-8dfcb10dd1f74775a3ea03e927c1b9232025-08-20T03:39:29ZengIEEEIEEE Access2169-35362025-01-0113929069292010.1109/ACCESS.2025.357101411006703Nearest-Neighbor Dual-Path Contrastive Learning for Lumbar Disc Herniation MRI Image ClassificationDan Pan0https://orcid.org/0009-0005-4698-6324Yu-Xiang Pan1Hui Wang2Qi-Jing Liu3Chang-Mao Qiu4Department of Rehabilitation Medicine, The First Affiliated Hospital of Gannan Medical University, Ganzhou, ChinaDepartment of Rehabilitation Medicine, Gannan Healthcare Vocational College, Ganzhou, ChinaDepartment of Pharmacy, Ganzhou People’s Hospital, Ganzhou, ChinaDepartment of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hung Hom, Hong Kong, ChinaMedical Imaging Center, The First Affiliated Hospital of Gannan Medical University, Ganzhou, ChinaLumbar disc herniation (LDH) is a common spinal condition that profoundly affects patients’ quality of life. Timely and precise diagnosis is essential for efficient therapy and enhancing patient outcomes. This paper introduces an innovative LDH classification framework utilizing nearest-neighbor dual-path contrastive learning, integrating global and local feature learning to improve lumbar MRI image classification efficacy. The global path delineates semantic linkages among samples by nearest-neighbor contrastive learning, enhancing global representations, whereas the local path employs clipped regions and data augmentation to highlight essential details, thus improving fine-grained feature modeling. The novel nearest-neighbor-based positive sample construction enhances feature consistency and classification accuracy by reducing the impact of irrelevant examples. Our method achieves state-of-the-art accuracy and robustness in complicated classification tasks, as shown by experimental results on the lumbar MRI dataset. This discovery enhances automated LDH diagnosis and offers a viable avenue for accurate and efficient automated diagnosis in intricate medical imaging contexts.https://ieeexplore.ieee.org/document/11006703/Lumbar spine disc classificationlumbar spinecontrastive learningdeep learning |
| spellingShingle | Dan Pan Yu-Xiang Pan Hui Wang Qi-Jing Liu Chang-Mao Qiu Nearest-Neighbor Dual-Path Contrastive Learning for Lumbar Disc Herniation MRI Image Classification IEEE Access Lumbar spine disc classification lumbar spine contrastive learning deep learning |
| title | Nearest-Neighbor Dual-Path Contrastive Learning for Lumbar Disc Herniation MRI Image Classification |
| title_full | Nearest-Neighbor Dual-Path Contrastive Learning for Lumbar Disc Herniation MRI Image Classification |
| title_fullStr | Nearest-Neighbor Dual-Path Contrastive Learning for Lumbar Disc Herniation MRI Image Classification |
| title_full_unstemmed | Nearest-Neighbor Dual-Path Contrastive Learning for Lumbar Disc Herniation MRI Image Classification |
| title_short | Nearest-Neighbor Dual-Path Contrastive Learning for Lumbar Disc Herniation MRI Image Classification |
| title_sort | nearest neighbor dual path contrastive learning for lumbar disc herniation mri image classification |
| topic | Lumbar spine disc classification lumbar spine contrastive learning deep learning |
| url | https://ieeexplore.ieee.org/document/11006703/ |
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