Ender Konukoglu

is this you? claim profile

∙ 1 follower
ETH Zurich

Assistant Professor of Biomedical Image Computing at ETH Zurich

Hyperspectral Image Super-Resolution with Spectral Mixup and Heterogeneous Datasets

This work studies Hyperspectral image (HSI) super-resolution (SR). HSI S...
01/19/2021 ∙ by Ke Li, et al. ∙ 17 ∙ share

read it
Probabilistic 3D surface reconstruction from sparse MRI information

Surface reconstruction from magnetic resonance (MR) imaging data is indi...
10/05/2020 ∙ by Katarína Tóthová, et al. ∙ 10 ∙ share

read it
Sampling possible reconstructions of undersampled acquisitions in MR imaging

Undersampling the k-space during MR acquisitions saves time, however res...
09/30/2020 ∙ by Kerem C. Tezcan, et al. ∙ 8 ∙ share

read it
RevPHiSeg: A Memory-Efficient Neural Network for Uncertainty Quantification in Medical Image Segmentation

Quantifying segmentation uncertainty has become an important issue in me...
08/16/2020 ∙ by Marc Gantenbein, et al. ∙ 7 ∙ share

read it
Joint reconstruction and bias field correction for undersampled MR imaging

Undersampling the k-space in MRI allows saving precious acquisition time...
07/26/2020 ∙ by Mélanie Gaillochet, et al. ∙ 16 ∙ share

read it
Semi-supervised Task-driven Data Augmentation for Medical Image Segmentation

Supervised learning-based segmentation methods typically require a large...
07/09/2020 ∙ by Krishna Chaitanya, et al. ∙ 53 ∙ share

read it
Modelling the Distribution of 3D Brain MRI using a 2D Slice VAE

Probabilistic modelling has been an essential tool in medical image anal...
07/09/2020 ∙ by Anna Volokitin, et al. ∙ 34 ∙ share

read it
Unsupervised out-of-distribution detection using kernel density estimation

Deep neural networks achieve significant advancement to the state-of-the...
06/18/2020 ∙ by Ertunc Erdil, et al. ∙ 9 ∙ share

read it
Contrastive learning of global and local features for medical image segmentation with limited annotations

A key requirement for the success of supervised deep learning is a large...
06/18/2020 ∙ by Krishna Chaitanya, et al. ∙ 6 ∙ share

read it
Unsupervised Lesion Detection via Image Restoration with a Normative Prior

Unsupervised lesion detection is a challenging problem that requires acc...
04/30/2020 ∙ by Xiaoran Chen, et al. ∙ 41 ∙ share

read it
Test-Time Adaptable Neural Networks for Robust Medical Image Segmentation

Convolutional Neural Networks (CNNs) work very well for supervised learn...
04/09/2020 ∙ by Neerav Karani, et al. ∙ 8 ∙ share

read it
Automated quantification of myocardial tissue characteristics from native T1 mapping using neural networks with Bayesian inference for uncertainty-based quality-control

Tissue characterisation with CMR parametric mapping has the potential to...
01/31/2020 ∙ by Esther Puyol-Antón, et al. ∙ 9 ∙ share

read it
Machine Learning with Multi-Site Imaging Data: An Empirical Study on the Impact of Scanner Effects

This is an empirical study to investigate the impact of scanner effects ...
10/10/2019 ∙ by Ben Glocker, et al. ∙ 22 ∙ share

read it
A Partially Reversible U-Net for Memory-Efficient Volumetric Image Segmentation

One of the key drawbacks of 3D convolutional neural networks for segment...
06/14/2019 ∙ by Robin Brügger, et al. ∙ 0 ∙ share

read it
PHiSeg: Capturing Uncertainty in Medical Image Segmentation

Segmentation of anatomical structures and pathologies is inherently ambi...
06/07/2019 ∙ by Christian F. Baumgartner, et al. ∙ 4 ∙ share

read it
Adversarial Augmentation for Enhancing Classification of Mammography Images

Supervised deep learning relies on the assumption that enough training d...
02/20/2019 ∙ by Lukas Jendele, et al. ∙ 6 ∙ share

read it
Semi-Supervised and Task-Driven Data Augmentation

Supervised deep learning methods for segmentation require large amounts ...
02/11/2019 ∙ by Krishna Chaitanya, et al. ∙ 18 ∙ share

read it
Injecting and removing malignant features in mammography with CycleGAN: Investigation of an automated adversarial attack using neural networks

Purpose To train a cycle-consistent generative adversarial network (Cycl...
11/19/2018 ∙ by Anton S. Becker, et al. ∙ 16 ∙ share

read it
Morpho-MNIST: Quantitative Assessment and Diagnostics for Representation Learning

Revealing latent structure in data is an active field of research, havin...
09/27/2018 ∙ by Daniel Coelho de Castro, et al. ∙ 22 ∙ share

read it
Uncertainty Quantification in CNN-Based Surface Prediction Using Shape Priors

Surface reconstruction is a vital tool in a wide range of areas of medic...
07/30/2018 ∙ by Katarína Tóthová, et al. ∙ 6 ∙ share

read it
Combining Heterogeneously Labeled Datasets For Training Segmentation Networks

Accurate segmentation of medical images is an important step towards ana...
07/24/2018 ∙ by Jana Kemnitz, et al. ∙ 2 ∙ share

read it
Iterative Interaction Training for Segmentation Editing Networks

Automatic segmentation has great potential to facilitate morphological m...
07/23/2018 ∙ by Gustav Bredell, et al. ∙ 2 ∙ share

read it
Generative Adversarial Networks for MR-CT Deformable Image Registration

Deformable Image Registration (DIR) of MR and CT images is one of the mo...
07/19/2018 ∙ by Christine Tanner, et al. ∙ 14 ∙ share

read it
Learning to Segment Medical Images with Scribble-Supervision Alone

Semantic segmentation of medical images is a crucial step for the quanti...
07/12/2018 ∙ by Yigit B. Can, et al. ∙ 6 ∙ share

read it
Deep Generative Models in the Real-World: An Open Challenge from Medical Imaging

Recent advances in deep learning led to novel generative modeling techni...
06/14/2018 ∙ by Xiaoran Chen, et al. ∙ 4 ∙ share

read it
Unsupervised Detection of Lesions in Brain MRI using constrained adversarial auto-encoders

Lesion detection in brain Magnetic Resonance Images (MRI) remains a chal...
06/13/2018 ∙ by Xiaoran Chen, et al. ∙ 2 ∙ share

read it
A Lifelong Learning Approach to Brain MR Segmentation Across Scanners and Protocols

Convolutional neural networks (CNNs) have shown promising results on sev...
05/25/2018 ∙ by Neerav Karani, et al. ∙ 0 ∙ share

read it
Temporal Interpolation via Motion Field Prediction

Navigated 2D multi-slice dynamic Magnetic Resonance (MR) imaging enables...
04/12/2018 ∙ by Lin Zhang, et al. ∙ 0 ∙ share

read it
MR image reconstruction using the learned data distribution as prior

MR image reconstruction from undersampled data exploits priors to compen...
11/30/2017 ∙ by Kerem C. Tezcan, et al. ∙ 0 ∙ share

read it
Visual Feature Attribution using Wasserstein GANs

Attributing the pixels of an input image to a certain category is an imp...
11/24/2017 ∙ by Christian F. Baumgartner, et al. ∙ 0 ∙ share

read it
An Exploration of 2D and 3D Deep Learning Techniques for Cardiac MR Image Segmentation

Accurate segmentation of the heart is an important step towards evaluati...
09/13/2017 ∙ by Christian F. Baumgartner, et al. ∙ 0 ∙ share

read it
Deep EndoVO: A Recurrent Convolutional Neural Network (RCNN) based Visual Odometry Approach for Endoscopic Capsule Robots

Ingestible wireless capsule endoscopy is an emerging minimally invasive ...
08/22/2017 ∙ by Mehmet Turan, et al. ∙ 0 ∙ share

read it
A fully dense and globally consistent 3D map reconstruction approach for GI tract to enhance therapeutic relevance of the endoscopic capsule robot

In the gastrointestinal (GI) tract endoscopy field, ingestible wireless ...
05/18/2017 ∙ by Mehmet Turan, et al. ∙ 0 ∙ share

read it
Magnetic-Visual Sensor Fusion based Medical SLAM for Endoscopic Capsule Robot

A reliable, real-time simultaneous localization and mapping (SLAM) metho...
05/17/2017 ∙ by Mehmet Turan, et al. ∙ 0 ∙ share

read it
A Non-Rigid Map Fusion-Based RGB-Depth SLAM Method for Endoscopic Capsule Robots

In the gastrointestinal (GI) tract endoscopy field, ingestible wireless ...
05/15/2017 ∙ by Mehmet Turan, et al. ∙ 0 ∙ share

read it
A Deep Learning Based 6 Degree-of-Freedom Localization Method for Endoscopic Capsule Robots

We present a robust deep learning based 6 degrees-of-freedom (DoF) local...
05/15/2017 ∙ by Mehmet Turan, et al. ∙ 0 ∙ share

read it
SubCMap: Subject and Condition Specific Effect Maps

Most widely used statistical analysis methods for neuroimaging data iden...
01/10/2017 ∙ by Ender Konukoglu, et al. ∙ 0 ∙ share

read it
WESD - Weighted Spectral Distance for Measuring Shape Dissimilarity

This article presents a new distance for measuring shape dissimilarity b...
08/24/2012 ∙ by Ender Konukoglu, et al. ∙ 0 ∙ share

read it

Ender Konukoglu

Featured Co-authors