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Improving Pediatric Low-Grade Neuroepithelial Tumors Molecular Subtype Identification Using a Novel AUROC Loss Function for Convolutional Neural Networks
The baseline model was trained using binary cross entropy (BCE), and achieved an AUROC of 86. 11% for differentiating BRAF fusion and BRAF V600E mutations, which was improved to 87. 71% using our proposed AUROC loss function (p-value 0. 045).
Generative Adversarial Networks for Weakly Supervised Generation and Evaluation of Brain Tumor Segmentations on MR Images
Segmentation of regions of interest (ROIs) for identifying abnormalities is a leading problem in medical imaging.
Open-radiomics: A Collection of Standardized Datasets and a Technical Protocol for Reproducible Radiomics Machine Learning Pipelines
Using PyRadiomics library for LGG vs. HGG classification, 288 radiomics datasets are formed; the combinations of 4 MRI sequences, 3 binWidths, 6 image normalization methods, and 4 tumor subregions.
Improving the Segmentation of Pediatric Low-Grade Gliomas through Multitask Learning
Brain tumor segmentation is a critical task for tumor volumetric analyses and AI algorithms.
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