no code implementations • 12 Apr 2023 • Gustav Bredell, Kyriakos Flouris, Krishna Chaitanya, Ertunc Erdil, Ender Konukoglu
Variational autoencoders (VAEs) are powerful generative modelling methods, however they suffer from blurry generated samples and reconstructions compared to the images they have been trained on.
1 code implementation • 10 Feb 2022 • Neerav Karani, Georg Brunner, Ertunc Erdil, Simin Fei, Kerem Tezcan, Krishna Chaitanya, Ender Konukoglu
We use 1D marginal distributions of a trained task CNN's features as experts in the FoE model.
1 code implementation • 19 Dec 2021 • Gustav Bredell, Ertunc Erdil, Bruno Weber, Ender Konukoglu
In addition, the image generator reproduces low-frequency features of the deconvolved image faster than that of a blurry image.
no code implementations • 17 Dec 2021 • Krishna Chaitanya, Ertunc Erdil, Neerav Karani, Ender Konukoglu
In this paper, we propose a local contrastive loss to learn good pixel level features useful for segmentation by exploiting semantic label information obtained from pseudo-labels of unlabeled images alongside limited annotated images.
1 code implementation • NeurIPS 2021 • Sara Sangalli, Ertunc Erdil, Andreas Hoetker, Olivio Donati, Ender Konukoglu
Such class imbalance is ubiquitous in clinical applications and very crucial to handle because the classes with fewer samples most often correspond to critical cases (e. g., cancer) where misclassifications can have severe consequences.
1 code implementation • 16 Aug 2020 • Marc Gantenbein, Ertunc Erdil, Ender Konukoglu
We incorporate the reversible blocks into a recently proposed architecture called PHiSeg that is developed for uncertainty quantification in medical image segmentation.
1 code implementation • 9 Jul 2020 • Anna Volokitin, Ertunc Erdil, Neerav Karani, Kerem Can Tezcan, Xiaoran Chen, Luc van Gool, Ender Konukoglu
We propose a method to model 3D MR brain volumes distribution by combining a 2D slice VAE with a Gaussian model that captures the relationships between slices.
1 code implementation • 9 Jul 2020 • Krishna Chaitanya, Neerav Karani, Christian F. Baumgartner, Ertunc Erdil, Anton Becker, Olivio Donati, Ender Konukoglu
In this work, we propose a novel task-driven data augmentation method for learning with limited labeled data where the synthetic data generator, is optimized for the segmentation task.
1 code implementation • 18 Jun 2020 • Ertunc Erdil, Krishna Chaitanya, Neerav Karani, Ender Konukoglu
The results demonstrate that the proposed method consistently achieves high OOD detection performance in both classification and segmentation tasks and improves state-of-the-art in almost all cases.
1 code implementation • NeurIPS 2020 • Krishna Chaitanya, Ertunc Erdil, Neerav Karani, Ender Konukoglu
In this work, we propose strategies for extending the contrastive learning framework for segmentation of volumetric medical images in the semi-supervised setting with limited annotations, by leveraging domain-specific and problem-specific cues.
2 code implementations • 9 Apr 2020 • Neerav Karani, Ertunc Erdil, Krishna Chaitanya, Ender Konukoglu
In medical image segmentation, this premise is violated when there is a mismatch between training and test images in terms of their acquisition details, such as the scanner model or the protocol.
no code implementations • 8 Jan 2019 • Ertunc Erdil, Ali Ozgur Argunsah, Tolga Tasdizen, Devrim Unay, Mujdat Cetin
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 shape and data terms can then work together to produce better segmentations.
no code implementations • 3 Sep 2018 • Ertunc Erdil, Sinan Yildirim, Tolga Tasdizen, Mujdat Cetin
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.
no code implementations • CVPR 2016 • Ertunc Erdil, Sinan Yildirim, Müjdat Çetin, Tolga Taşdizen
With a statistical view, addressing these issues would involve the problem of characterizing the posterior densities of the shapes of the objects to be segmented.
no code implementations • 19 Jul 2016 • Muhammad Usman Ghani, Ertunc Erdil, Sumeyra Demir Kanik, Ali Ozgur Argunsah, Anna Felicity Hobbiss, Inbal Israely, Devrim Unay, Tolga Tasdizen, Mujdat Cetin
We perform cluster analysis on two-photon microscopic images of spines using morphological, shape, and appearance based features and gain insights into the spine shape analysis problem.