Ender Konukoglu

research

∙ 08/09/2023

Expert load matters: operating networks at high accuracy and low manual effort

In human-AI collaboration systems for critical applications, in order to...

0 Sara Sangalli, et al. ∙

research

∙ 08/03/2023

Quantification of Predictive Uncertainty via Inference-Time Sampling

Predictive variability due to data ambiguities has typically been addres...

0 Katarína Tóthová, et al. ∙

research

∙ 04/12/2023

Explicitly Minimizing the Blur Error of Variational Autoencoders

Variational autoencoders (VAEs) are powerful generative modelling method...

4 Gustav Bredell, et al. ∙

research

∙ 03/31/2023

Live image-based neurosurgical guidance and roadmap generation using unsupervised embedding

Advanced minimally invasive neurosurgery navigation relies mainly on Mag...

0 Gary Sarwin, et al. ∙

research

∙ 02/20/2023

InOR-Net: Incremental 3D Object Recognition Network for Point Cloud Representation

3D object recognition has successfully become an appealing research topi...

0 Jiahua Dong, et al. ∙

research

∙ 01/30/2023

FedFA: Federated Feature Augmentation

Federated learning is a distributed paradigm that allows multiple partie...

0 Tianfei Zhou, et al. ∙

research

∙ 11/28/2022

Unsupervised Superpixel Generation using Edge-Sparse Embedding

Partitioning an image into superpixels based on the similarity of pixels...

0 Jakob Geusen, et al. ∙

research

∙ 04/13/2022

Neural Vector Fields for Surface Representation and Inference

Neural implicit fields have recently been shown to represent 3D shapes a...

0 Edoardo Mello Rella, et al. ∙

research

∙ 03/28/2022

Rethinking Semantic Segmentation: A Prototype View

Prevalent semantic segmentation solutions, despite their different netwo...

0 Tianfei Zhou, et al. ∙

research

∙ 03/16/2022

Zero Pixel Directional Boundary by Vector Transform

Boundaries are among the primary visual cues used by human and computer ...

0 Edoardo Mello Rella, et al. ∙

research

∙ 02/10/2022

A Field of Experts Prior for Adapting Neural Networks at Test Time

Performance of convolutional neural networks (CNNs) in image analysis ta...

14 Neerav Karani, et al. ∙

research

∙ 12/19/2021

Wiener Guided DIP for Unsupervised Blind Image Deconvolution

Blind deconvolution is an ill-posed problem arising in various fields ra...

11 Gustav Bredell, et al. ∙

research

∙ 12/17/2021

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

Supervised deep learning-based methods yield accurate results for medica...

6 Krishna Chaitanya, et al. ∙

research

∙ 11/27/2021

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

Recent works show that convolutional neural network (CNN) architectures ...

0 Metin Ersin Arican, et al. ∙

research

∙ 06/20/2021

Quality-Aware Memory Network for Interactive Volumetric Image Segmentation

Despite recent progress of automatic medical image segmentation techniqu...

0 Tianfei Zhou, et al. ∙

research

∙ 05/11/2021

Gradient flow encoding with distance optimization adaptive step size

The autoencoder model uses an encoder to map data samples to a lower dim...

0 Kyriakos Flouris, et al. ∙

research

∙ 03/30/2021

Contrastive Learning of Single-Cell Phenotypic Representations for Treatment Classification

Learning robust representations to discriminate cell phenotypes based on...

0 Alexis Perakis, et al. ∙

research

∙ 02/21/2021

Constrained Optimization for Training Deep Neural Networks Under Class Imbalance

Deep neural networks (DNNs) are notorious for making more mistakes for t...

52 Sara Sangalli, et al. ∙

research

∙ 01/28/2021

Exploring Cross-Image Pixel Contrast for Semantic Segmentation

Current semantic segmentation methods focus only on mining "local" conte...

0 Wenguan Wang, et al. ∙

research

∙ 01/19/2021

Hyperspectral Image Super-Resolution with Spectral Mixup and Heterogeneous Datasets

This work studies Hyperspectral image (HSI) super-resolution (SR). HSI S...

17 Ke Li, et al. ∙

research

∙ 10/05/2020

Probabilistic 3D surface reconstruction from sparse MRI information

Surface reconstruction from magnetic resonance (MR) imaging data is indi...

10 Katarína Tóthová, et al. ∙

research

∙ 09/30/2020

Sampling possible reconstructions of undersampled acquisitions in MR imaging

Undersampling the k-space during MR acquisitions saves time, however res...

8 Kerem C. Tezcan, et al. ∙

research

∙ 08/16/2020

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

Quantifying segmentation uncertainty has become an important issue in me...

7 Marc Gantenbein, et al. ∙

research

∙ 07/26/2020

Joint reconstruction and bias field correction for undersampled MR imaging

Undersampling the k-space in MRI allows saving precious acquisition time...

16 Mélanie Gaillochet, et al. ∙

research

∙ 07/09/2020

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

Supervised learning-based segmentation methods typically require a large...

53 Krishna Chaitanya, et al. ∙

research

∙ 07/09/2020

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

Probabilistic modelling has been an essential tool in medical image anal...

34 Anna Volokitin, et al. ∙

research

∙ 06/18/2020

Unsupervised out-of-distribution detection using kernel density estimation

Deep neural networks achieve significant advancement to the state-of-the...

9 Ertunc Erdil, et al. ∙

research

∙ 06/18/2020

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...

6 Krishna Chaitanya, et al. ∙

research

∙ 04/30/2020

Unsupervised Lesion Detection via Image Restoration with a Normative Prior

Unsupervised lesion detection is a challenging problem that requires acc...

41 Xiaoran Chen, et al. ∙

research

∙ 04/09/2020

Test-Time Adaptable Neural Networks for Robust Medical Image Segmentation

Convolutional Neural Networks (CNNs) work very well for supervised learn...

8 Neerav Karani, et al. ∙

research

∙ 01/31/2020

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...

9 Esther Puyol-Antón, et al. ∙

research

∙ 10/10/2019

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 ...

22 Ben Glocker, et al. ∙

research

∙ 06/14/2019

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

One of the key drawbacks of 3D convolutional neural networks for segment...

0 Robin Brügger, et al. ∙

research

∙ 06/07/2019

PHiSeg: Capturing Uncertainty in Medical Image Segmentation

Segmentation of anatomical structures and pathologies is inherently ambi...

4 Christian F. Baumgartner, et al. ∙

research

∙ 02/20/2019

Adversarial Augmentation for Enhancing Classification of Mammography Images

Supervised deep learning relies on the assumption that enough training d...

6 Lukas Jendele, et al. ∙

research

∙ 02/11/2019

Semi-Supervised and Task-Driven Data Augmentation

Supervised deep learning methods for segmentation require large amounts ...

18 Krishna Chaitanya, et al. ∙

research

∙ 11/19/2018

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...

16 Anton S. Becker, et al. ∙

research

∙ 09/27/2018

Morpho-MNIST: Quantitative Assessment and Diagnostics for Representation Learning

Revealing latent structure in data is an active field of research, havin...

22 Daniel Coelho de Castro, et al. ∙

research

∙ 07/30/2018

Uncertainty Quantification in CNN-Based Surface Prediction Using Shape Priors

Surface reconstruction is a vital tool in a wide range of areas of medic...

6 Katarína Tóthová, et al. ∙

research

∙ 07/24/2018

Combining Heterogeneously Labeled Datasets For Training Segmentation Networks

Accurate segmentation of medical images is an important step towards ana...

2 Jana Kemnitz, et al. ∙

research

∙ 07/23/2018

Iterative Interaction Training for Segmentation Editing Networks

Automatic segmentation has great potential to facilitate morphological m...

2 Gustav Bredell, et al. ∙

research

∙ 07/19/2018

Generative Adversarial Networks for MR-CT Deformable Image Registration

Deformable Image Registration (DIR) of MR and CT images is one of the mo...

14 Christine Tanner, et al. ∙

research

∙ 07/12/2018

Learning to Segment Medical Images with Scribble-Supervision Alone

Semantic segmentation of medical images is a crucial step for the quanti...

6 Yigit B. Can, et al. ∙

research

∙ 06/14/2018

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

Recent advances in deep learning led to novel generative modeling techni...

4 Xiaoran Chen, et al. ∙

research

∙ 06/13/2018

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

Lesion detection in brain Magnetic Resonance Images (MRI) remains a chal...

2 Xiaoran Chen, et al. ∙

research

∙ 05/25/2018

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

Convolutional neural networks (CNNs) have shown promising results on sev...

0 Neerav Karani, et al. ∙

research

∙ 04/12/2018

Temporal Interpolation via Motion Field Prediction

Navigated 2D multi-slice dynamic Magnetic Resonance (MR) imaging enables...

0 Lin Zhang, et al. ∙

research

∙ 11/30/2017

MR image reconstruction using the learned data distribution as prior

MR image reconstruction from undersampled data exploits priors to compen...

0 Kerem C. Tezcan, et al. ∙

research

∙ 11/24/2017

Visual Feature Attribution using Wasserstein GANs

Attributing the pixels of an input image to a certain category is an imp...

0 Christian F. Baumgartner, et al. ∙

research

∙ 09/13/2017

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...

0 Christian F. Baumgartner, et al. ∙

Ender Konukoglu

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