162 citations · 455 across the 51 of their papers we have counts for
6 papers · 1 filter
Analyzing an Imitation Learning Network for Fundus Image Registration Using a Divide-and-Conquer Approach
Siming Bayer, Xia Zhong, Weilin Fu +2
Comparison of microvascular circulation on fundoscopic images is a non-invasive clinical indication for the diagnosis and monitoring of diseases, such as diabetes and hypertensions…
Coronary Artery Plaque Characterization from CCTA Scans using Deep Learning and Radiomics
Felix Denzinger, Michael Wels, Nishant Ravikumar +6
Assessing coronary artery plaque segments in coronary CT angiography scans is an important task to improve patient management and clinical outcomes, as it can help to decide whethe…
Automated Multi-sequence Cardiac MRI Segmentation Using Supervised Domain Adaptation
Sulaiman Vesal, Nishant Ravikumar, Andreas Maier
Left ventricle segmentation and morphological assessment are essential for improving diagnosis and our understanding of cardiomyopathy, which in turn is imperative for reducing ris…
A Divide-and-Conquer Approach towards Understanding Deep Networks
Weilin Fu, Katharina Breininger, Roman Schaffert +2
Deep neural networks have achieved tremendous success in various fields including medical image segmentation. However, they have long been criticized for being a black-box, in that…
A 2D dilated residual U-Net for multi-organ segmentation in thoracic CT
Sulaiman Vesal, Nishant Ravikumar, Andreas Maier
Automatic segmentation of organs-at-risk (OAR) in computed tomography (CT) is an essential part of planning effective treatment strategies to combat lung and esophageal cancer. Acc…
Dilated deeply supervised networks for hippocampus segmentation in MRI
Lukas Folle, Sulaiman Vesal, Nishant Ravikumar +1
Tissue loss in the hippocampi has been heavily correlated with the progression of Alzheimer's Disease (AD). The shape and structure of the hippocampus are important factors in term…