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Found 4 papers, 2 papers with code
Postoperative glioblastoma segmentation: Development of a fully automated pipeline using deep convolutional neural networks and comparison with currently available models
Accurately assessing tumor removal is paramount in the management of glioblastoma.
A Fully Automated Pipeline Using Swin Transformers for Deep Learning-Based Blood Segmentation on Head CT Scans After Aneurysmal Subarachnoid Hemorrhage
Methods: We retrospectively analyzed NCCT scans from patients with confirmed aneurysmal subarachnoid hemorrhage (aSAH) utilizing the Swin UNETR for segmentation.
Enhanced Mortality Prediction In Patients With Subarachnoid Haemorrhage Using A Deep Learning Model Based On The Initial CT Scan
Our objective was to predict mortality in SAH patients by processing the initial CT scan on a CNN based algorithm.
The Rio Hortega University Hospital Glioblastoma dataset: a comprehensive collection of preoperative, early postoperative and recurrence MRI scans (RHUH-GBM)
Glioblastoma, a highly aggressive primary brain tumor, is associated with poor patient outcomes.
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