3 citations · 5 across the 2 of their papers we have counts for
4 papers
An Uncertainty-Driven GCN Refinement Strategy for Organ Segmentation
Roger D. Soberanis-Mukul, Nassir Navab, Shadi Albarqouni
Organ segmentation in CT volumes is an important pre-processing step in many computer assisted intervention and diagnosis methods. In recent years, convolutional neural networks ha…
Polyp-artifact relationship analysis using graph inductive learned representations
Roger D. Soberanis-Mukul, Shadi Albarqouni, Nassir Navab
The diagnosis process of colorectal cancer mainly focuses on the localization and characterization of abnormal growths in the colon tissue known as polyps. Despite recent advances…
Understanding the effects of artifacts on automated polyp detection and incorporating that knowledge via learning without forgetting
Maxime Kayser, Roger D. Soberanis-Mukul, Anna-Maria Zvereva +3
Survival rates for colorectal cancer are higher when polyps are detected at an early stage and can be removed before they develop into malignant tumors. Automated polyp detection,…
Uncertainty-based graph convolutional networks for organ segmentation refinement
Roger D. Soberanis-Mukul, Nassir Navab, Shadi Albarqouni
Organ segmentation in CT volumes is an important pre-processing step in many computer assisted intervention and diagnosis methods. In recent years, convolutional neural networks ha…