Radboudumc

∙ Nijmegen , Netherlands ∙ 0 followers

The Radboudumc wants to be at the forefront of the development of innovative, sustainable and affordable healthcare.

Featured Researchers

Bram van Ginneken
37 publications
Geert Litjens
17 publications
Jonas Adler
12 publications
Jeroen van der Laak
12 publications
Jonas Teuwen
11 publications
Francesco Ciompi
11 publications
Mohsen Ghafoorian
10 publications
Nikolas Lessmann
10 publications
Wouter Bulten
7 publications
Nikita Moriakov
6 publications
Babak Ehteshami Bejnordi
5 publications

Kernel of CycleGAN as a Principle homogeneous space

Unpaired image-to-image translation has attracted significant interest d...
01/24/2020 ∙ by Nikita Moriakov, et al. ∙ 25 ∙ share

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Contextual Two-Stage U-Nets for Robust Pulmonary Lobe Segmentation in CT Scans of COVID-19 and COPD Patients

Pulmonary lobe segmentation in computed tomography scans is essential fo...
04/16/2020 ∙ by Weiyi Xie, et al. ∙ 17 ∙ share

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Neural Image Compression for Gigapixel Histopathology Image Analysis

We present Neural Image Compression (NIC), a method to reduce the size o...
11/07/2018 ∙ by David Tellez, et al. ∙ 12 ∙ share

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Streaming convolutional neural networks for end-to-end learning with multi-megapixel images

Due to memory constraints on current hardware, most convolution neural n...
11/11/2019 ∙ by Hans Pinckaers, et al. ∙ 7 ∙ share

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Context-aware stacked convolutional neural networks for classification of breast carcinomas in whole-slide histopathology images

Automated classification of histopathological whole-slide images (WSI) o...
05/10/2017 ∙ by Babak Ehteshami Bejnordi, et al. ∙ 0 ∙ share

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Transfer Learning for Domain Adaptation in MRI: Application in Brain Lesion Segmentation

Magnetic Resonance Imaging (MRI) is widely used in routine clinical diag...
02/25/2017 ∙ by Mohsen Ghafoorian, et al. ∙ 0 ∙ share

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Deep Multi-scale Location-aware 3D Convolutional Neural Networks for Automated Detection of Lacunes of Presumed Vascular Origin

Lacunes of presumed vascular origin (lacunes) are associated with an inc...
10/24/2016 ∙ by Mohsen Ghafoorian, et al. ∙ 0 ∙ share

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Location Sensitive Deep Convolutional Neural Networks for Segmentation of White Matter Hyperintensities

The anatomical location of imaging features is of crucial importance for...
10/16/2016 ∙ by Mohsen Ghafoorian, et al. ∙ 0 ∙ share

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Student Beats the Teacher: Deep Neural Networks for Lateral Ventricles Segmentation in Brain MR

Ventricular volume and its progression are known to be linked to several...
01/15/2018 ∙ by Mohsen Ghafoorian, et al. ∙ 0 ∙ share

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Automated soft tissue lesion detection and segmentation in digital mammography using a u-net deep learning network

Computer-aided detection or decision support systems aim to improve brea...
02/19/2018 ∙ by Timothy de Moor, et al. ∙ 0 ∙ share

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Training convolutional neural networks with megapixel images

To train deep convolutional neural networks, the input data and the inte...
04/16/2018 ∙ by Hans Pinckaers, et al. ∙ 0 ∙ share

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Unsupervised Prostate Cancer Detection on H&E using Convolutional Adversarial Autoencoders

We propose an unsupervised method using self-clustering convolutional ad...
04/19/2018 ∙ by Wouter Bulten, et al. ∙ 0 ∙ share

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Detecting SNPs with interactive effects on a quantitative trait

Here we propose a test to detect effects of single nucleotide polymorphi...
05/23/2018 ∙ by Armin Rauschenberger, et al. ∙ 0 ∙ share

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Deep Learning Framework for Digital Breast Tomosynthesis Reconstruction

Digital breast tomosynthesis is rapidly replacing digital mammography as...
08/14/2018 ∙ by Nikita Moriakov, et al. ∙ 0 ∙ share

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Vendor-independent soft tissue lesion detection using weakly supervised and unsupervised adversarial domain adaptation

Computer-aided detection aims to improve breast cancer screening program...
08/14/2018 ∙ by Joris van Vugt, et al. ∙ 0 ∙ share

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Whole-Slide Mitosis Detection in H&E Breast Histology Using PHH3 as a Reference to Train Distilled Stain-Invariant Convolutional Networks

Manual counting of mitotic tumor cells in tissue sections constitutes on...
08/17/2018 ∙ by David Tellez, et al. ∙ 0 ∙ share

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Chest X-ray Inpainting with Deep Generative Models

Generative adversarial networks have been successfully applied to inpain...
08/29/2018 ∙ by Ecem Sogancioglu, et al. ∙ 0 ∙ share

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GANs for Medical Image Analysis

Generative Adversarial Networks (GANs) and their extensions have carved ...
09/13/2018 ∙ by Salome Kazeminia, et al. ∙ 0 ∙ share

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Comparison of U-net-based Convolutional Neural Networks for Liver Segmentation in CT

Various approaches for liver segmentation in CT have been proposed: Besi...
10/09/2018 ∙ by Hans Meine, et al. ∙ 0 ∙ share

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Chest X-Rays Image Inpainting with Context Encoders

Chest X-rays are one of the most commonly used technologies for medical ...
12/03/2018 ∙ by Davide Belli, et al. ∙ 0 ∙ share

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Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology

Stain variation is a phenomenon observed when distinct pathology laborat...
02/18/2019 ∙ by David Tellez, et al. ∙ 0 ∙ share

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Dealing with Label Scarcity in Computational Pathology: A Use Case in Prostate Cancer Classification

Large amounts of unlabelled data are commonplace for many applications i...
05/16/2019 ∙ by Koen Dercksen, et al. ∙ 0 ∙ share

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FRODO: Free rejection of out-of-distribution samples: application to chest x-ray analysis

In this work, we propose a method to reject out-of-distribution samples ...
07/02/2019 ∙ by Erdi Çallı, et al. ∙ 0 ∙ share

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A powerful MAF-neutral allele-based test for case-control association studies

In a case-control study aimed at locating autosomal disease variants for...
02/12/2020 ∙ by Marianne A. Jonker, et al. ∙ 0 ∙ share

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Random smooth gray value transformations for cross modality learning with gray value invariant networks

Random transformations are commonly used for augmentation of the trainin...
03/13/2020 ∙ by Nikolas Lessmann, et al. ∙ 0 ∙ share

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Extending Unsupervised Neural Image Compression With Supervised Multitask Learning

We focus on the problem of training convolutional neural networks on gig...
04/15/2020 ∙ by David Tellez, et al. ∙ 0 ∙ share

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Inferring astrophysical X-ray polarization with deep learning

We investigate the use of deep learning in the context of X-ray polariza...
05/16/2020 ∙ by Nikita Moriakov, et al. ∙ 0 ∙ share

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Detection of prostate cancer in whole-slide images through end-to-end training with image-level labels

Prostate cancer is the most prevalent cancer among men in Western countr...
06/05/2020 ∙ by Hans Pinckaers, et al. ∙ 0 ∙ share

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Deep learning reconstruction of digital breast tomosynthesis images for accurate breast density and patient-specific radiation dose estimation

The two-dimensional nature of mammography makes estimation of the overal...
06/11/2020 ∙ by Jonas Teuwen, et al. ∙ 0 ∙ share

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Deep Learning with robustness to missing data: A novel approach to the detection of COVID-19

In the context of the current global pandemic and the limitations of the...
03/25/2021 ∙ by Erdi Çallı, et al. ∙ 0 ∙ share

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