241 citations · 264 across the 11 of their papers we have counts for
14 papers
Expert load matters: operating networks at high accuracy and low manual effort
Sara Sangalli, Ertunc Erdil, Ender Konukoglu
In human-AI collaboration systems for critical applications, in order to ensure minimal error, users should set an operating point based on model confidence to determine when the d…
Explicitly Minimizing the Blur Error of Variational Autoencoders
Gustav Bredell, Kyriakos Flouris, Krishna Chaitanya +2
Variational autoencoders (VAEs) are powerful generative modelling methods, however they suffer from blurry generated samples and reconstructions compared to the images they have be…
A Field of Experts Prior for Adapting Neural Networks at Test Time
Neerav Karani, Georg Brunner, Ertunc Erdil +4
Performance of convolutional neural networks (CNNs) in image analysis tasks is often marred in the presence of acquisition-related distribution shifts between training and test ima…
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…
RevPHiSeg: A Memory-Efficient Neural Network for Uncertainty Quantification in Medical Image Segmentation
Marc Gantenbein, Ertunc Erdil, Ender Konukoglu
Quantifying segmentation uncertainty has become an important issue in medical image analysis due to the inherent ambiguity of anatomical structures and its pathologies. Recently, n…
Modelling the Distribution of 3D Brain MRI using a 2D Slice VAE
Anna Volokitin, Ertunc Erdil, Neerav Karani +4
Probabilistic modelling has been an essential tool in medical image analysis, especially for analyzing brain Magnetic Resonance Images (MRI). Recent deep learning techniques for es…
Task-agnostic Out-of-Distribution Detection Using Kernel Density Estimation
Ertunc Erdil, Krishna Chaitanya, Neerav Karani +1
In the recent years, researchers proposed a number of successful methods to perform out-of-distribution (OOD) detection in deep neural networks (DNNs). So far the scope of the high…
Contrastive learning of global and local features for medical image segmentation with limited annotations
Krishna Chaitanya, Ertunc Erdil, Neerav Karani +1
A key requirement for the success of supervised deep learning is a large labeled dataset - a condition that is difficult to meet in medical image analysis. Self-supervised learning…
Combining nonparametric spatial context priors with nonparametric shape priors for dendritic spine segmentation in 2-photon microscopy images
Ertunc Erdil, Ali Ozgur Argunsah, Tolga Tasdizen +2
Data driven segmentation is an important initial step of shape prior-based segmentation methods since it is assumed that the data term brings a curve to a plausible level so that s…
Image Segmentation with Pseudo-marginal MCMC Sampling and Nonparametric Shape Priors
Ertunc Erdil, Sinan Yildirim, Tolga Tasdizen +1
In this paper, we propose an efficient pseudo-marginal Markov chain Monte Carlo (MCMC) sampling approach to draw samples from posterior shape distributions for image segmentation.…
MCMC Shape Sampling for Image Segmentation with Nonparametric Shape Priors
Ertunc Erdil, Sinan Yıldırım, Müjdat Çetin +1
Segmenting images of low quality or with missing data is a challenging problem. Integrating statistical prior information about the shapes to be segmented can improve the segmentat…
Dendritic Spine Shape Analysis: A Clustering Perspective
Muhammad Usman Ghani, Ertunc Erdil, Sumeyra Demir Kanik +6
Functional properties of neurons are strongly coupled with their morphology. Changes in neuronal activity alter morphological characteristics of dendritic spines. First step toward…