https://www.selleckchem.com/pr....oducts/ag-1478-tyrph
To improve the robustness of deep learning-based glioblastoma segmentation in a clinical setting with sparsified datasets. In this retrospective study, preoperative T1-weighted, T2-weighted, T2-weighted fluid-attenuated inversion recovery, and postcontrast T1-weighted MRI from 117 patients (median age, 64 years; interquartile range [IQR], 55-73 years; 76 men) included within the Multimodal Brain Tumor Image Segmentation (BraTS) dataset plus a clinical dataset (2012-2013) with similar imaging modalities of 634 patients
Like
Comment
Share
