The brain tumor segmentation (BraTS) challenge 2023: focus on pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs)
AF Kazerooni, N Khalili, X Liu, D Haldar, Z Jiang… - Ar** of pediatric low-grade glioma with self-supervised transfer learning
Purpose To develop and externally test a scan-to-prediction deep learning pipeline for
noninvasive, MRI-based BRAF mutational status classification for pediatric low-grade …
noninvasive, MRI-based BRAF mutational status classification for pediatric low-grade …
Training and Comparison of nnU-Net and DeepMedic Methods for Autosegmentation of Pediatric Brain Tumors
A Vossough, N Khalili, AM Familiar… - American Journal …, 2024 - Am Soc Neuroradiology
BACKGROUND AND PURPOSE: Tumor segmentation is essential in surgical and treatment
planning and response assessment and monitoring in pediatric brain tumors, the leading …
planning and response assessment and monitoring in pediatric brain tumors, the leading …
[HTML][HTML] Expert-level pediatric brain tumor segmentation in a limited data scenario with stepwise transfer learning
Purpose Artificial intelligence (AI)-automated tumor delineation for pediatric gliomas would
enable real-time volumetric evaluation to support diagnosis, treatment response …
enable real-time volumetric evaluation to support diagnosis, treatment response …
A review of deep learning for brain tumor analysis in MRI
Recent progress in deep learning (DL) is producing a new generation of tools across
numerous clinical applications. Within the analysis of brain tumors in magnetic resonance …
numerous clinical applications. Within the analysis of brain tumors in magnetic resonance …
3D-TransUNet for brain metastases segmentation in the BraTS2023 challenge
Segmenting brain tumors is complex due to their diverse appearances and scales. Brain
metastases, the most common type of brain tumor, are a frequent complication of cancer …
metastases, the most common type of brain tumor, are a frequent complication of cancer …