KEXNet: A Knowledge-Enhanced Model for Improved Chest X-Ray Lesion Detection
Automated diagnosis of chest X-rays is pivotal in radiology, aiming to alleviate the workload of radiologists. Traditional methods primarily rely on visual features or label dependence, which is a limitation in detecting nuanced or rare lesions. To address this, we present KEXNet, a pioneering knowl...
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| Format: | Article |
| Language: | English |
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Tsinghua University Press
2024-12-01
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| Series: | Big Data Mining and Analytics |
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| Online Access: | https://www.sciopen.com/article/10.26599/BDMA.2024.9020045 |
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| author | Quan Yan Junwen Duan Jianxin Wang |
| author_facet | Quan Yan Junwen Duan Jianxin Wang |
| author_sort | Quan Yan |
| collection | DOAJ |
| description | Automated diagnosis of chest X-rays is pivotal in radiology, aiming to alleviate the workload of radiologists. Traditional methods primarily rely on visual features or label dependence, which is a limitation in detecting nuanced or rare lesions. To address this, we present KEXNet, a pioneering knowledge-enhanced X-ray lesion detection model. KEXNet employs a unique strategy akin to expert radiologists, integrating a knowledge graph based on expert annotations with an interpretable graph learning approach. This novel method combines object detection with a graph neural network, facilitating precise local lesion detection. For global lesion detection, KEXNet synergizes knowledge-enhanced local features with global image features, enhancing diagnostic accuracy. Our evaluations on three benchmark datasets demonstrate that KEXNet outshines existing models, particularly in identifying small or infrequent lesions. Notably, on the Chest ImaGenome dataset, KEXNet’s AUC for local lesion detection surpasses 8.9% compared to the state-of-the-art method AnaXNet, showcasing its potential in revolutionizing automated chest X-ray diagnostics. |
| format | Article |
| id | doaj-art-95c49182c7304bcdac3282f5fccf9e44 |
| institution | OA Journals |
| issn | 2096-0654 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Big Data Mining and Analytics |
| spelling | doaj-art-95c49182c7304bcdac3282f5fccf9e442025-08-20T01:58:28ZengTsinghua University PressBig Data Mining and Analytics2096-06542024-12-01741187119810.26599/BDMA.2024.9020045KEXNet: A Knowledge-Enhanced Model for Improved Chest X-Ray Lesion DetectionQuan Yan0Junwen Duan1Jianxin Wang2School of Computer Science and Engineering and Hunan Provincial Key Lab on Bioinformatics, Central South University, Changsha 410083, ChinaSchool of Computer Science and Engineering and Hunan Provincial Key Lab on Bioinformatics, Central South University, Changsha 410083, ChinaSchool of Computer Science and Engineering and Hunan Provincial Key Lab on Bioinformatics, Central South University, Changsha 410083, ChinaAutomated diagnosis of chest X-rays is pivotal in radiology, aiming to alleviate the workload of radiologists. Traditional methods primarily rely on visual features or label dependence, which is a limitation in detecting nuanced or rare lesions. To address this, we present KEXNet, a pioneering knowledge-enhanced X-ray lesion detection model. KEXNet employs a unique strategy akin to expert radiologists, integrating a knowledge graph based on expert annotations with an interpretable graph learning approach. This novel method combines object detection with a graph neural network, facilitating precise local lesion detection. For global lesion detection, KEXNet synergizes knowledge-enhanced local features with global image features, enhancing diagnostic accuracy. Our evaluations on three benchmark datasets demonstrate that KEXNet outshines existing models, particularly in identifying small or infrequent lesions. Notably, on the Chest ImaGenome dataset, KEXNet’s AUC for local lesion detection surpasses 8.9% compared to the state-of-the-art method AnaXNet, showcasing its potential in revolutionizing automated chest X-ray diagnostics.https://www.sciopen.com/article/10.26599/BDMA.2024.9020045multi-label chest x-ray classificationobject detectionknowledge graph learning |
| spellingShingle | Quan Yan Junwen Duan Jianxin Wang KEXNet: A Knowledge-Enhanced Model for Improved Chest X-Ray Lesion Detection Big Data Mining and Analytics multi-label chest x-ray classification object detection knowledge graph learning |
| title | KEXNet: A Knowledge-Enhanced Model for Improved Chest X-Ray Lesion Detection |
| title_full | KEXNet: A Knowledge-Enhanced Model for Improved Chest X-Ray Lesion Detection |
| title_fullStr | KEXNet: A Knowledge-Enhanced Model for Improved Chest X-Ray Lesion Detection |
| title_full_unstemmed | KEXNet: A Knowledge-Enhanced Model for Improved Chest X-Ray Lesion Detection |
| title_short | KEXNet: A Knowledge-Enhanced Model for Improved Chest X-Ray Lesion Detection |
| title_sort | kexnet a knowledge enhanced model for improved chest x ray lesion detection |
| topic | multi-label chest x-ray classification object detection knowledge graph learning |
| url | https://www.sciopen.com/article/10.26599/BDMA.2024.9020045 |
| work_keys_str_mv | AT quanyan kexnetaknowledgeenhancedmodelforimprovedchestxraylesiondetection AT junwenduan kexnetaknowledgeenhancedmodelforimprovedchestxraylesiondetection AT jianxinwang kexnetaknowledgeenhancedmodelforimprovedchestxraylesiondetection |