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Found 5 papers, 0 papers with code
Radiopathomics: Integration of radiographic and histologic characteristics for prognostication in glioblastoma
An extensive set of engineered features was extracted from delineated tumor regions in Rad images, comprising T1, T1-Gd, T2, T2-FLAIR, and 100 random patches extracted from Path images.
Prediction of overall survival and molecular markers in gliomas via analysis of digital pathology images using deep learning
In this paper, we developed a computational approach based on deep learning to predict the overall survival and molecular subtypes of glioma patients from microscopic images of tissue biopsies, reflecting measures of microvascular proliferation, mitotic activity, nuclear atypia, and the presence of necrosis.
Deep radiomic features from MRI scans predict survival outcome of recurrent glioblastoma
This paper proposes to use deep radiomic features (DRFs) from a convolutional neural network (CNN) to model fine-grained texture signatures in the radiomic analysis of recurrent glioblastoma (rGBM).
Deep radiomic signature with immune cell markers predicts the survival of glioma patients
These results demonstrate the usefulness of proposed DRFs as non-invasive biomarker for predicting treatment response in patients with brain tumors.
A Recent Survey of Vision Transformers for Medical Image Segmentation
This survey paper provides a detailed review of the recent advancements in ViTs and HVTs for medical image segmentation.
