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

2024/07/01 by Aidan Boyd, Zezhong Ye, Sanjay P. Prabhu +15 · 1 voice · 3 citations
Neuroscience · Medicine · Computer Science · #Brain Tumor Detection and Classification #COVID-19 diagnosis using AI #Advanced Neural Network Applications

paper · doi:10.1148/ryai.230254

openalex publication_date 2024/07/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/22

Abstract

To develop, externally test, and evaluate clinical acceptability of a deep learning pediatric brain tumor segmentation model using stepwise transfer learning.

Citations

• The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)
• Automated Design of Deep Learning Methods for Biomedical Image Segmentation
• A Comprehensive Survey on Transfer Learning
• Swin-Unet: Unet-Like Pure Transformer for Medical Image Segmentation

Cited by

• Multi-Institutional Annotated Multiparametric MRI Dataset of Pediatric High-Grade Gliomas
• Pediatrics 4.0: the Transformative Impacts of the Latest Industrial Revolution on Pediatrics
• Enhancing efficiency in paediatric brain tumour segmentation using a pathologically diverse single-center clinical dataset

Discussions

• #ThrowbackThursday – #DeepLearning MRI segmentation model for pediatric low-grade #glioma demonstrated strong performance (Jul 2024) https://doi.org/10.1148/ryai.230254 #MachineLearning #Radiomics #Ar [bsky, 3 points, 0 comments]

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• Unified HT-CNNs Architecture: Transfer Learning for Segmenting Diverse Brain Tumors in MRI from Gliomas to Pediatric Tumors