no code implementations • 1 Dec 2023 • Asifullah Khan, Zunaira Rauf, Abdul Rehman Khan, Saima Rathore, Saddam Hussain Khan, Najmus Saher Shah, Umair Farooq, Hifsa Asif, Aqsa Asif, Umme Zahoora, Rafi Ullah Khalil, Suleman Qamar, Umme Hani Asif, Faiza Babar Khan, Abdul Majid, Jeonghwan Gwak
This survey paper provides a detailed review of the recent advancements in ViTs and HVTs for medical image segmentation.
no code implementations • 9 Jun 2022 • Ahmad Chaddad, Paul Daniel Mingli Zhang, Saima Rathore, Paul Sargos, Christian Desrosiers, Tamim Niazi
These results demonstrate the usefulness of proposed DRFs as non-invasive biomarker for predicting treatment response in patients with brain tumors.
no code implementations • 15 Nov 2019 • Ahmad Chaddad, Saima Rathore, Mingli Zhang, Christian Desrosiers, Tamim Niazi
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).
no code implementations • 19 Sep 2019 • Saima Rathore, Muhammad Aksam Iftikhar, Zissimos Mourelatos
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.
no code implementations • 17 Sep 2019 • Saima Rathore, Muhammad A. Iftikhar, Metin N. Gurcan, Zissimos Mourelatos
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.