Search Results for author: Ender Konukoglu

Found 47 papers, 24 papers with code

Local contrastive loss with pseudo-label based self-training for semi-supervised medical image segmentation

no code implementations17 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.

Medical Image Segmentation Self-Supervised Learning

ISNAS-DIP: Image-Specific Neural Architecture Search for Deep Image Prior

no code implementations27 Nov 2021 Metin Ersin Arican, Ozgur Kara, Gustav Bredell, Ender Konukoglu

Our experiments show that image-specific metrics can reduce the search space to a small cohort of models, of which the best model outperforms current NAS approaches for image restoration.

Image Denoising Image Restoration +2

Quality-Aware Memory Network for Interactive Volumetric Image Segmentation

1 code implementation20 Jun 2021 Tianfei Zhou, Liulei Li, Gustav Bredell, Jianwu Li, Ender Konukoglu

The proposed network has two appealing characteristics: 1) The memory-augmented network offers the ability to quickly encode past segmentation information, which will be retrieved for the segmentation of other slices; 2) The quality assessment module enables the model to directly estimate the qualities of segmentation predictions, which allows an active learning paradigm where users preferentially label the lowest-quality slice for multi-round refinement.

Active Learning Interactive Segmentation +1

Gradient flow encoding with distance optimization adaptive step size

no code implementations11 May 2021 Kyriakos Flouris, Anna Volokitin, Gustav Bredell, Ender Konukoglu

In this work, we investigate a decoder-only method that uses gradient flow to encode data samples in the latent space.

Constrained Optimization to Train Neural Networks on Critical and Under-Represented Classes

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.

Multi-class Classification

Exploring Cross-Image Pixel Contrast for Semantic Segmentation

4 code implementations ICCV 2021 Wenguan Wang, Tianfei Zhou, Fisher Yu, Jifeng Dai, Ender Konukoglu, Luc van Gool

Inspired by the recent advance in unsupervised contrastive representation learning, we propose a pixel-wise contrastive framework for semantic segmentation in the fully supervised setting.

Metric Learning Optical Character Recognition +2

Hyperspectral Image Super-Resolution with Spectral Mixup and Heterogeneous Datasets

2 code implementations19 Jan 2021 Ke Li, Dengxin Dai, Ender Konukoglu, Luc van Gool

With these contributions, our method is able to learn from heterogeneous datasets and lift the requirement for having a large amount of HD HSI training samples.

Data Augmentation Hyperspectral Image Super-Resolution +1

Sampling possible reconstructions of undersampled acquisitions in MR imaging

1 code implementation30 Sep 2020 Kerem C. Tezcan, Neerav Karani, Christian F. Baumgartner, Ender Konukoglu

In this paper, we propose a method that instead returns multiple images which are possible under the acquisition model and the chosen prior to capture the uncertainty in the inversion process.

Image Reconstruction

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

1 code implementation16 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.

Medical Image Segmentation

Joint reconstruction and bias field correction for undersampled MR imaging

no code implementations26 Jul 2020 Mélanie Gaillochet, Kerem C. Tezcan, Ender Konukoglu

To this end, we use an unsupervised learning based reconstruction algorithm as our basis and combine it with a N4-based bias field estimation method, in a joint optimization scheme.

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

1 code implementation9 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.

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

1 code implementation9 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.

Data Augmentation Medical Image Segmentation

Task-agnostic Out-of-Distribution Detection Using Kernel Density Estimation

1 code implementation18 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.

Autonomous Driving Density Estimation +4

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

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.

Contrastive Learning Data Augmentation +2

Unsupervised Lesion Detection via Image Restoration with a Normative Prior

no code implementations30 Apr 2020 Xiaoran Chen, Suhang You, Kerem Can Tezcan, Ender Konukoglu

In this work, we approach unsupervised lesion detection as an image restoration problem and propose a probabilistic model that uses a network-based prior as the normative distribution and detect lesions pixel-wise using MAP estimation.

Image Restoration

Test-Time Adaptable Neural Networks for Robust Medical Image Segmentation

2 code implementations9 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.

Denoising Domain Generalization +1

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

no code implementations10 Oct 2019 Ben Glocker, Robert Robinson, Daniel C. Castro, Qi Dou, Ender Konukoglu

This is an empirical study to investigate the impact of scanner effects when using machine learning on multi-site neuroimaging data.

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

3 code implementations14 Jun 2019 Robin Brügger, Christian F. Baumgartner, Ender Konukoglu

Increasing network depth led to higher segmentation accuracy while growing the memory footprint only by a very small fraction, thanks to the partially reversible architecture.

Image Classification Medical Image Segmentation

Medical Imaging with Deep Learning: MIDL 2019 -- Extended Abstract Track

no code implementations21 May 2019 M. Jorge Cardoso, Aasa Feragen, Ben Glocker, Ender Konukoglu, Ipek Oguz, Gozde Unal, Tom Vercauteren

This compendium gathers all the accepted extended abstracts from the Second International Conference on Medical Imaging with Deep Learning (MIDL 2019), held in London, UK, 8-10 July 2019.

Adversarial Augmentation for Enhancing Classification of Mammography Images

1 code implementation20 Feb 2019 Lukas Jendele, Ondrej Skopek, Anton S. Becker, Ender Konukoglu

Supervised deep learning relies on the assumption that enough training data is available, which presents a problem for its application to several fields, like medical imaging.

General Classification Image Augmentation +1

Semi-Supervised and Task-Driven Data Augmentation

1 code implementation11 Feb 2019 Krishna Chaitanya, Neerav Karani, Christian Baumgartner, Olivio Donati, Anton Becker, Ender Konukoglu

However, there is potential to improve the approach by (i) explicitly modeling deformation fields (non-affine spatial transformation) and intensity transformations and (ii) leveraging unlabelled data during the generative process.

Data Augmentation

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

2 code implementations19 Nov 2018 Anton S. Becker, Lukas Jendele, Ondrej Skopek, Nicole Berger, Soleen Ghafoor, Magda Marcon, Ender Konukoglu

At the higher resolution, all radiologists showed significantly lower detection rate of cancer in the modified images (0. 77-0. 84 vs. 0. 59-0. 69, p=0. 008), however, they were now able to reliably detect modified images due to better visibility of artifacts (0. 92, 0. 92 and 0. 97).

Adversarial Attack

Morpho-MNIST: Quantitative Assessment and Diagnostics for Representation Learning

1 code implementation ICLR 2019 Daniel C. Castro, Jeremy Tan, Bernhard Kainz, Ender Konukoglu, Ben Glocker

Revealing latent structure in data is an active field of research, having introduced exciting technologies such as variational autoencoders and adversarial networks, and is essential to push machine learning towards unsupervised knowledge discovery.

Domain Adaptation Outlier Detection +1

Combining Heterogeneously Labeled Datasets For Training Segmentation Networks

no code implementations24 Jul 2018 Jana Kemnitz, Christian F. Baumgartner, Wolfgang Wirth, Felix Eckstein, Sebastian K. Eder, Ender Konukoglu

In this work we propose a cost function which allows integration of multiple datasets with heterogeneous label subsets into a joint training.

Iterative Interaction Training for Segmentation Editing Networks

no code implementations23 Jul 2018 Gustav Bredell, Christine Tanner, Ender Konukoglu

Automatic segmentation has great potential to facilitate morphological measurements while simultaneously increasing efficiency.

Interactive Segmentation

Generative Adversarial Networks for MR-CT Deformable Image Registration

no code implementations19 Jul 2018 Christine Tanner, Firat Ozdemir, Romy Profanter, Valeriy Vishnevsky, Ender Konukoglu, Orcun Goksel

Performance for the abdominal region was similar to that of CT-MRI NMI registration (77. 4 vs. 78. 8%) when using 3D synthesizing MRIs (12 slices) and medium sized receptive fields for the discriminator.

Image Generation Image Registration

Learning to Segment Medical Images with Scribble-Supervision Alone

no code implementations12 Jul 2018 Yigit B. Can, Krishna Chaitanya, Basil Mustafa, Lisa M. Koch, Ender Konukoglu, Christian F. Baumgartner

We find that the networks trained on scribbles suffer from a remarkably small degradation in Dice of only 2. 9% (cardiac) and 4. 5% (prostate) with respect to a network trained on full annotations.

Medical Image Segmentation

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

no code implementations14 Jun 2018 Xiaoran Chen, Nick Pawlowski, Martin Rajchl, Ben Glocker, Ender Konukoglu

In this paper, we explore the feasibility of using state-of-the-art auto-encoder-based deep generative models, such as variational and adversarial auto-encoders, for one such task: abnormality detection in medical imaging.

Anomaly Detection

Temporal Interpolation via Motion Field Prediction

1 code implementation12 Apr 2018 Lin Zhang, Neerav Karani, Christine Tanner, Ender Konukoglu

Temporal interpolation of navigator slices an be used to reduce the number of navigator acquisitions without degrading specificity in stacking.

MR image reconstruction using deep density priors

no code implementations30 Nov 2017 Kerem C. Tezcan, Christian F. Baumgartner, Roger Luechinger, Klaas P. Pruessmann, Ender Konukoglu

Deep learning (DL) provides a powerful framework for extracting such information from existing image datasets, through learning, and then using it for reconstruction.

Density Estimation Image Reconstruction

Visual Feature Attribution using Wasserstein GANs

3 code implementations CVPR 2018 Christian F. Baumgartner, Lisa M. Koch, Kerem Can Tezcan, Jia Xi Ang, Ender Konukoglu

Attributing the pixels of an input image to a certain category is an important and well-studied problem in computer vision, with applications ranging from weakly supervised localisation to understanding hidden effects in the data.

Sparse-then-Dense Alignment based 3D Map Reconstruction Method for Endoscopic Capsule Robots

no code implementations29 Aug 2017 Mehmet Turan, Yusuf Yigit Pilavci, Ipek Ganiyusufoglu, Helder Araujo, Ender Konukoglu, Metin Sitti

Since the development of capsule endoscopcy technology, substantial progress were made in converting passive capsule endoscopes to robotic active capsule endoscopes which can be controlled by the doctor.

3D Reconstruction

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

no code implementations22 Aug 2017 Mehmet Turan, Yasin Almalioglu, Helder Araujo, Ender Konukoglu, Metin Sitti

Ingestible wireless capsule endoscopy is an emerging minimally invasive diagnostic technology for inspection of the GI tract and diagnosis of a wide range of diseases and pathologies.

Monocular Visual Odometry Pose Estimation

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

no code implementations18 May 2017 Mehmet Turan, Yusuf Yigit Pilavci, Redhwan Jamiruddin, Helder Araujo, Ender Konukoglu, Metin Sitti

In the gastrointestinal (GI) tract endoscopy field, ingestible wireless capsule endoscopy is emerging as a novel, minimally invasive diagnostic technology for inspection of the GI tract and diagnosis of a wide range of diseases and pathologies.

3D Reconstruction Image Registration +1

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

no code implementations17 May 2017 Mehmet Turan, Yasin Almalioglu, Hunter Gilbert, Helder Araujo, Ender Konukoglu, Metin Sitti

A reliable, real-time simultaneous localization and mapping (SLAM) method is crucial for the navigation of actively controlled capsule endoscopy robots.

Simultaneous Localization and Mapping Surface Reconstruction

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

no code implementations15 May 2017 Mehmet Turan, Yasin Almalioglu, Helder Araujo, Ender Konukoglu, Metin Sitti

In this paper, we propose to our knowledge for the first time in literature a visual simultaneous localization and mapping (SLAM) method specifically developed for endoscopic capsule robots.

Simultaneous Localization and Mapping

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

no code implementations15 May 2017 Mehmet Turan, Yasin Almalioglu, Ender Konukoglu, Metin Sitti

We present a robust deep learning based 6 degrees-of-freedom (DoF) localization system for endoscopic capsule robots.


Reconstructing Subject-Specific Effect Maps

1 code implementation10 Jan 2017 Ender Konukoglu, Ben Glocker

Analyses on the ADNI dataset show that RSM can also improve correlation between subject-specific detections in cortical thickness data and non-imaging markers of Alzheimer's Disease (AD), such as the Mini Mental State Examination Score and Cerebrospinal Fluid amyloid-$\beta$ levels.

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