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Improving Automated Pediatric Bone Age Estimation Using Ensembles of Models from the 2017 RSNA Machine Learning Challenge
PURPOSE: To investigate improvements in performance for automatic bone age estimation that can be gained through model ensembling. MATERIALS AND METHODS: A total of 48 submissions from the 2017 RSNA Pediatric Bone Age Machine Learning Challenge were used. Participants were provided with 12 611 pedia...
Tallennettuna:
| Julkaisussa: | Radiol Artif Intell |
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| Päätekijät: | , , , , |
| Aineistotyyppi: | Artigo |
| Kieli: | Inglês |
| Julkaistu: |
Radiological Society of North America
2019
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| Aiheet: | |
| Linkit: | https://ncbi.nlm.nih.gov/pmc/articles/PMC6884060/ https://ncbi.nlm.nih.gov/pubmed/32090207 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1148/ryai.2019190053 |
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