241 citations · 264 across the 11 of their papers we have counts for
3 papers · 1 filter
Wiener Guided DIP for Unsupervised Blind Image Deconvolution
Gustav Bredell, Ertunc Erdil, Bruno Weber +1
Blind deconvolution is an ill-posed problem arising in various fields ranging from microscopy to astronomy. The ill-posed nature of the problem requires adequate priors to arrive t…
Local contrastive loss with pseudo-label based self-training for semi-supervised medical image segmentation
Krishna Chaitanya, Ertunc Erdil, Neerav Karani +1
Supervised deep learning-based methods yield accurate results for medical image segmentation. However, they require large labeled datasets for this, and obtaining them is a laborio…
Constrained Optimization to Train Neural Networks on Critical and Under-Represented Classes
Sara Sangalli, Ertunc Erdil, Andreas Hoetker +2
Deep neural networks (DNNs) are notorious for making more mistakes for the classes that have substantially fewer samples than the others during training. Such class imbalance is ub…