Stepwise Transfer Learning for Expert-level Pediatric Brain Tumor MRI Segmentation in a Limited Data Scenario-Reference-Cited by-同舟云学术

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Stepwise Transfer Learning for Expert-level Pediatric Brain Tumor MRI Segmentation in a Limited Data Scenario

Published:2024-07-01 Issue:4 Volume:6 Page:
ISSN:2638-6100
Container-title:Radiology: Artificial Intelligence
language:en
Short-container-title:Radiology: Artificial Intelligence

Author:

Boyd Aidan^ORCID,Ye Zezhong^ORCID,Prabhu Sanjay P.^ORCID,Tjong Michael C.^ORCID,Zha Yining^ORCID,Zapaishchykova Anna^ORCID,Vajapeyam Sridhar^ORCID,Catalano Paul J.,Hayat Hasaan^ORCID,Chopra Rishi^ORCID,Liu Kevin X.^ORCID,Nabavizadeh Ali^ORCID,Resnick Adam C.,Mueller Sabine,Haas-Kogan Daphne A.^ORCID,Aerts Hugo J. W. L.^ORCID,Poussaint Tina Y.^ORCID,Kann Benjamin H.^ORCID

Funder

National Institutes of Health

National Cancer Institute Specialized Programs of Research Excellence

Radiological Society of North America

Publisher

Radiological Society of North America (RSNA)

Link

http://pubs.rsna.org/doi/pdf/10.1148/ryai.230254

Reference42 articles.

1. CBTRUS Statistical Report: Pediatric Brain Tumor Foundation Childhood and Adolescent Primary Brain and Other Central Nervous System Tumors Diagnosed in the United States in 2014–2018

2. Pediatric low-grade glioma in the era of molecular diagnostics

3. Pediatric low-grade glioma: Targeted therapeutics and clinical trials in the molecular era

4. Long‐term outcome of 4,040 children diagnosed with pediatric low‐grade gliomas: An analysis of the Surveillance Epidemiology and End Results (SEER) database

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