Ender Konukoglu

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ETH Zurich

Assistant Professor of Biomedical Image Computing at ETH Zurich

Contrastive Learning of Single-Cell Phenotypic Representations for Treatment Classification

Learning robust representations to discriminate cell phenotypes based on...
03/30/2021 ∙ by Alexis Perakis, et al. ∙ 0 ∙ share

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Constrained Optimization for Training Deep Neural Networks Under Class Imbalance

Deep neural networks (DNNs) are notorious for making more mistakes for t...
02/21/2021 ∙ by Sara Sangalli, et al. ∙ 52 ∙ share

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Exploring Cross-Image Pixel Contrast for Semantic Segmentation

Current semantic segmentation methods focus only on mining "local" conte...
01/28/2021 ∙ by Wenguan Wang, et al. ∙ 0 ∙ share

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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Ender Konukoglu

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