26 citations · 51 across the 5 of their papers we have counts for
5 papers
A Recent Survey of Vision Transformers for Medical Image Segmentation
Asifullah Khan, Zunaira Rauf, Abdul Rehman Khan +13
Medical image segmentation plays a crucial role in various healthcare applications, enabling accurate diagnosis, treatment planning, and disease monitoring. Traditionally, convolut…
Deep radiomic signature with immune cell markers predicts the survival of glioma patients
Ahmad Chaddad, Paul Daniel Mingli Zhang, Saima Rathore +3
Imaging biomarkers offer a non-invasive way to predict the response of immunotherapy prior to treatment. In this work, we propose a novel type of deep radiomic features (DRFs) comp…
Deep radiomic features from MRI scans predict survival outcome of recurrent glioblastoma
Ahmad Chaddad, Saima Rathore, Mingli Zhang +2
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 gl…
Prediction of overall survival and molecular markers in gliomas via analysis of digital pathology images using deep learning
Saima Rathore, Muhammad Aksam Iftikhar, Zissimos Mourelatos
Cancer histology reveals disease progression and associated molecular processes, and contains rich phenotypic information that is predictive of outcome. In this paper, we developed…
Radiopathomics: Integration of radiographic and histologic characteristics for prognostication in glioblastoma
Saima Rathore, Muhammad A. Iftikhar, Metin N. Gurcan +1
Both radiographic (Rad) imaging, such as multi-parametric magnetic resonance imaging, and digital pathology (Path) images captured from tissue samples are currently acquired as sta…