The rsna-asnr-miccai brats 2021 benchmark on brain tumor segmentation and radiogenomic classification
The BraTS 2021 challenge celebrates its 10th anniversary and is jointly organized by the
Radiological Society of North America (RSNA), the American Society of Neuroradiology …
Radiological Society of North America (RSNA), the American Society of Neuroradiology …
Federated learning enables big data for rare cancer boundary detection
Although machine learning (ML) has shown promise across disciplines, out-of-sample
generalizability is concerning. This is currently addressed by sharing multi-site data, but …
generalizability is concerning. This is currently addressed by sharing multi-site data, but …
Within-modality synthesis and novel radiomic evaluation of brain MRI scans
Simple Summary Brain MRI scans often require different imaging sequences based on
tissue types, posing a common challenge. In our research, we propose a method that utilizes …
tissue types, posing a common challenge. In our research, we propose a method that utilizes …
The University of Pennsylvania glioblastoma (UPenn-GBM) cohort: advanced MRI, clinical, genomics, & radiomics
Glioblastoma is the most common aggressive adult brain tumor. Numerous studies have
reported results from either private institutional data or publicly available datasets. However …
reported results from either private institutional data or publicly available datasets. However …
The brain tumor segmentation (BraTS) challenge 2023: focus on pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs)
Pediatric tumors of the central nervous system are the most common cause of cancer-related
death in children. The five-year survival rate for high-grade gliomas in children is less than …
death in children. The five-year survival rate for high-grade gliomas in children is less than …
The brain tumor segmentation (brats-mets) challenge 2023: Brain metastasis segmentation on pre-treatment mri
The translation of AI-generated brain metastases (BM) segmentation into clinical practice
relies heavily on diverse, high-quality annotated medical imaging datasets. The BraTS …
relies heavily on diverse, high-quality annotated medical imaging datasets. The BraTS …
Radiogenomic classification for MGMT promoter methylation status using multi-omics fused feature space for least invasive diagnosis through mpMRI scans
Accurate radiogenomic classification of brain tumors is important to improve the standard of
diagnosis, prognosis, and treatment planning for patients with glioblastoma. In this study, we …
diagnosis, prognosis, and treatment planning for patients with glioblastoma. In this study, we …
The federated tumor segmentation (fets) challenge
This manuscript describes the first challenge on Federated Learning, namely the Federated
Tumor Segmentation (FeTS) challenge 2021. International challenges have become the …
Tumor Segmentation (FeTS) challenge 2021. International challenges have become the …
Fused deep learning paradigm for the prediction of o6-methylguanine-DNA methyltransferase genotype in glioblastoma patients: a neuro-oncological investigation
Abstract Background The O6-methylguanine-DNA methyltransferase (MGMT) is a
deoxyribonucleic acid (DNA) repairing enzyme that has been established as an essential …
deoxyribonucleic acid (DNA) repairing enzyme that has been established as an essential …
Clinical measures, radiomics, and genomics offer synergistic value in AI-based prediction of overall survival in patients with glioblastoma
Multi-omic data, ie, clinical measures, radiomic, and genetic data, capture multi-faceted
tumor characteristics, contributing to a comprehensive patient risk assessment. Here, we …
tumor characteristics, contributing to a comprehensive patient risk assessment. Here, we …