Ertunc Erdil
Ertunc Erdil
Postdoctoral Researcher, Computer Vision Laboratory, ETH Zurich
Verified email at - Homepage
Cited by
Cited by
Contrastive learning of global and local features for medical image segmentation with limited annotations
K Chaitanya, E Erdil, N Karani, E Konukoglu
Advances in neural information processing systems 33, 12546-12558, 2020
Test-Time Adaptable Neural Networks for Robust Medical Image Segmentation
N Karani, E Erdil, K Chaitanya, E Konukoglu
Medical Image Analysis, 101907, 2020
Semi-supervised Task-driven Data Augmentation for Medical Image Segmentation
K Chaitanya, N Karani, CF Baumgartner, E Erdil, A Becker, O Donati, ...
arXiv preprint arXiv:2007.05363, 2020
Combining multiple clusterings using similarity graph
S Mimaroglu, E Erdil
Pattern Recognition 44 (3), 694-703, 2011
Local contrastive loss with pseudo-label based self-training for semi-supervised medical image segmentation
K Chaitanya, E Erdil, N Karani, E Konukoglu
Medical image analysis 87, 102792, 2023
Modelling the distribution of 3D brain MRI using a 2D slice VAE
A Volokitin, E Erdil, N Karani, KC Tezcan, X Chen, L Van Gool, ...
Medical Image Computing and Computer Assisted Intervention–MICCAI 2020: 23rd …, 2020
Constrained optimization to train neural networks on critical and under-represented classes
S Sangalli, E Erdil, A Hötker, O Donati, E Konukoglu
Advances in neural information processing systems 34, 25400-25411, 2021
Nonparametric joint shape and feature priors for image segmentation
E Erdil, MU Ghani, L Rada, AO Argunsah, D Unay, T Tasdizen, M Cetin
IEEE Transactions on Image Processing 26 (11), 5312-5323, 2017
MCMC shape sampling for image segmentation with nonparametric shape priors
E Erdil, S Yildirim, M Cetin, T Tasdizen
Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2016
An efficient and scalable family of algorithms for combining clusterings
S Mimaroglu, E Erdil
Engineering Applications of Artificial Intelligence 26 (10), 2525-2539, 2013
Obtaining better quality final clustering by merging a collection of clusterings
S Mimaroglu, E Erdil
Bioinformatics 26 (20), 2645-2646, 2010
Image segmentation using disjunctive normal Bayesian shape and appearance models
F Mesadi, E Erdil, M Cetin, T Tasdizen
IEEE transactions on medical imaging 37 (1), 293-305, 2017
A joint classification and segmentation approach for dendritic spine segmentation in 2-photon microscopy images
E Erdil, AO Argunsah, T Tasdizen, D Unay, M Cetin
2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI), 797-800, 2015
A tool for automatic dendritic spine detection and analysis. part i: Dendritic spine detection using multi-level region-based segmentation
E Erdil, AM Yagci, AÖ Argunsah, Y Ramiro-Cortés, AF Hobbiss, I Israely, ...
2012 3rd International Conference on Image Processing Theory, Tools and …, 2012
Automatic dendritic spine detection using multiscale dot enhancement filters and sift features
L Rada, E Erdil, AO Argunsah, D Unay, M Cetin
2014 IEEE International Conference on Image Processing (ICIP), 26-30, 2014
ASOD: Arbitrary shape object detection
S Mimaroglu, E Erdil
Engineering Applications of Artificial Intelligence 24 (7), 1295-1299, 2011
Explicitly minimizing the blur error of variational autoencoders
G Bredell, K Flouris, K Chaitanya, E Erdil, E Konukoglu
arXiv preprint arXiv:2304.05939, 2023
Task-agnostic out-of-distribution detection using kernel density estimation
E Erdil, K Chaitanya, N Karani, E Konukoglu
arXiv preprint arXiv:2006.10712, 2021
Tracking-assisted detection of dendritic spines in time-lapse microscopic images
L Rada, B Kilic, E Erdil, Y Ramiro-Cortés, I Israely, D Unay, M Cetin, ...
Neuroscience 394, 189-205, 2018
Dendritic spine shape analysis: A clustering perspective
MU Ghani, E Erdil, SD Kanık, AÖ Argunşah, AF Hobbiss, I Israely, D Ünay, ...
Computer Vision–ECCV 2016 Workshops: Amsterdam, The Netherlands, October 8 …, 2016
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