11 citations · 25 across the 14 of their papers we have counts for
7 papers · 1 filter
TransResNet: Integrating the Strengths of ViTs and CNNs for High Resolution Medical Image Segmentation via Feature Grafting
Muhammad Hamza Sharif, Dmitry Demidov, Asif Hanif +2
High-resolution images are preferable in medical imaging domain as they significantly improve the diagnostic capability of the underlying method. In particular, high resolution hel…
A Radiogenomics Pipeline for Lung Nodules Segmentation and Prediction of EGFR Mutation Status from CT Scans
Ivo Gollini Navarrete, Mohammad Yaqub
Lung cancer is a leading cause of death worldwide. Early-stage detection of lung cancer is essential for a more favorable prognosis. Radiogenomics is an emerging discipline that co…
TMSS: An End-to-End Transformer-based Multimodal Network for Segmentation and Survival Prediction
Numan Saeed, Ikboljon Sobirov, Roba Al Majzoub +1
When oncologists estimate cancer patient survival, they rely on multimodal data. Even though some multimodal deep learning methods have been proposed in the literature, the majorit…
Color Space-based HoVer-Net for Nuclei Instance Segmentation and Classification
Hussam Azzuni, Muhammad Ridzuan, Min Xu +1
Nuclei segmentation and classification is the first and most crucial step that is utilized for many different microscopy medical analysis applications. However, it suffers from man…
Is it Possible to Predict MGMT Promoter Methylation from Brain Tumor MRI Scans using Deep Learning Models?
Numan Saeed, Shahad Hardan, Kudaibergen Abutalip +1
Glioblastoma is a common brain malignancy that tends to occur in older adults and is almost always lethal. The effectiveness of chemotherapy, being the standard treatment for most…
An Ensemble Approach for Patient Prognosis of Head and Neck Tumor Using Multimodal Data
Numan Saeed, Roba Al Majzoub, Ikboljon Sobirov +1
Accurate prognosis of a tumor can help doctors provide a proper course of treatment and, therefore, save the lives of many. Traditional machine learning algorithms have been eminen…