RadImageNet and ImageNet as Datasets for Transfer Learning in the Assessment of Dental Radiographs: A Comparative Study.
Transfer learning (TL) is an alternative approach to the full training of deep learning (DL) models from scratch and can transfer knowledge gained from large-scale data to solve different problems. ImageNet, which is a publicly available large-scale dataset, is a commonly used dataset for TL-based i...
| Publicado en: | Journal of Imaging Informatics in Medicine Vol. 38; no. 1; pp. 534 - 545 |
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| Autores principales: | , , , , , , , , , , , , |
| Formato: | diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Feb2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=184471491&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 184471491 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 29482925 NR3A jtl: Journal of Imaging Informatics in Medicine issn: 29482925 maglogo: N pubinfo: dt: Feb2025 vid: 38 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 184471491 184471491 184471491 10.1007/s10278-024-01204-9 184471491 ppf: 534 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: RadImageNet and ImageNet as Datasets for Transfer Learning in the Assessment of Dental Radiographs: A Comparative Study. aug: au: Okazaki, Shota Mine, Yuichi Yoshimi, Yuki Iwamoto, Yuko Ito, Shota Peng, Tzu-Yu Nishimura, Taku Suehiro, Tomoya Koizumi, Yuma Nomura, Ryota Tanimoto, Kotaro Kakimoto, Naoya Murayama, Takeshi affil: https://ror.org/03t78wx29 Department of Medical Systems Engineering, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan sug: subj: Radiography, Dental Methods Deep Learning Data Collection Machine Learning Diagnosis, Computer Assisted Methods Tooth Diseases Radiography Prediction Models Human Male Female Child, Preschool Child Adolescence Young Adult Comparative Studies Image Processing, Computer Assisted Algorithms Artificial Intelligence Natural Language Processing Tooth, Supernumerary Radiography Radiography, Panoramic Methods Sex Factors Cephalometry ROC Curve Descriptive Statistics Data Analysis Software Confidence Intervals Child, Preschool: 2-5 years Child: 6-12 years Adolescent: 13-18 years Male Female ab: Transfer learning (TL) is an alternative approach to the full training of deep learning (DL) models from scratch and can transfer knowledge gained from large-scale data to solve different problems. ImageNet, which is a publicly available large-scale dataset, is a commonly used dataset for TL-based image analysis; many studies have applied pre-trained models from ImageNet to clinical prediction tasks and have reported promising results. However, some have questioned the effectiveness of using ImageNet, which consists solely of natural images, for medical image analysis. The aim of this study was to evaluate whether pre-trained models using RadImageNet, which is a large-scale medical image dataset, could achieve superior performance in classification tasks in dental imaging modalities compared with ImageNet pre-trained models. To evaluate the classification performance of RadImageNet and ImageNet pre-trained models for TL, two dental imaging datasets were used. The tasks were (1) classifying the presence or absence of supernumerary teeth from a dataset of panoramic radiographs and (2) classifying sex from a dataset of lateral cephalometric radiographs. Performance was evaluated by comparing the area under the curve (AUC). On the panoramic radiograph dataset, the RadImageNet models gave average AUCs of 0.68 ± 0.15 (p < 0.01), and the ImageNet models had values of 0.74 ± 0.19. In contrast, on the lateral cephalometric dataset, the RadImageNet models demonstrated average AUCs of 0.76 ± 0.09, and the ImageNet models achieved values of 0.75 ± 0.17. The difference in performance between RadImageNet and ImageNet models in TL depends on the dental image dataset used. pubtype: Academic Journal doctype: diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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