دورية أكاديمية
Supervised representation learning based on various levels of pediatric radiographic views for transfer learning
| العنوان: | Supervised representation learning based on various levels of pediatric radiographic views for transfer learning |
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| المؤلفون: | Sunggu Kyung, Miso Jang, Seungju Park, Hee Mang Yoon, Gil-Sun Hong, Namkug Kim |
| المصدر: | Scientific Reports, Vol 14, Iss 1, Pp 1-11 (2024) |
| بيانات النشر: | Nature Portfolio, 2024. |
| سنة النشر: | 2024 |
| المجموعة: | LCC:Medicine LCC:Science |
| مصطلحات موضوعية: | Pediatric radiographs, Representation learning, Transfer learning, Bone age assessment, Deep learning, Fracture classification, Medicine, Science |
| الوصف: | Abstract Transfer learning plays a pivotal role in addressing the paucity of data, expediting training processes, and enhancing model performance. Nonetheless, the prevailing practice of transfer learning predominantly relies on pre-trained models designed for the natural image domain, which may not be well-suited for the medical image domain in grayscale. Recognizing the significance of leveraging transfer learning in medical research, we undertook the construction of class-balanced pediatric radiograph datasets collectively referred to as PedXnets, grounded in radiographic views using the pediatric radiographs collected over 24 years at Asan Medical Center. For PedXnets pre-training, approximately 70,000 X-ray images were utilized. Three different pre-training weights of PedXnet were constructed using Inception V3 for various radiation perspective classifications: Model-PedXnet-7C, Model-PedXnet-30C, and Model-PedXnet-68C. We validated the transferability and positive effects of transfer learning of PedXnets through pediatric downstream tasks including fracture classification and bone age assessment (BAA). The evaluation of transfer learning effects through classification and regression metrics showed superior performance of Model-PedXnets in quantitative assessments. Additionally, visual analyses confirmed that the Model-PedXnets were more focused on meaningful regions of interest. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2045-2322 |
| Relation: | https://doaj.org/toc/2045-2322 |
| DOI: | 10.1038/s41598-024-58163-y |
| URL الوصول: | https://doaj.org/article/22a4afdf7bea47209761ce87162adc55 |
| رقم الأكسشن: | edsdoj.22a4afdf7bea47209761ce87162adc55 |
| قاعدة البيانات: | Directory of Open Access Journals |
Find this article in full text from Springer Verlag. 
Full Text Finder | تدمد: | 20452322 |
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| DOI: | 10.1038/s41598-024-58163-y |