b'Radboudumc'

research

∙ 01/24/2020

Kernel of CycleGAN as a Principle homogeneous space

Unpaired image-to-image translation has attracted significant interest d...

Nikita Moriakov, et al. ∙

research

∙ 04/16/2020

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...

Weiyi Xie, et al. ∙

research

∙ 11/07/2018

Neural Image Compression for Gigapixel Histopathology Image Analysis

We present Neural Image Compression (NIC), a method to reduce the size o...

David Tellez, et al. ∙

research

∙ 01/12/2022

Structure and position-aware graph neural network for airway labeling

We present a novel graph-based approach for labeling the anatomical bran...

Weiyi Xie, et al. ∙

research

∙ 11/11/2019

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

Due to memory constraints on current hardware, most convolution neural n...

Hans Pinckaers, et al. ∙

research

∙ 05/10/2017

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...

Babak Ehteshami Bejnordi, et al. ∙

research

∙ 02/25/2017

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

Magnetic Resonance Imaging (MRI) is widely used in routine clinical diag...

Mohsen Ghafoorian, et al. ∙

research

∙ 10/24/2016

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...

Mohsen Ghafoorian, et al. ∙

research

∙ 10/16/2016

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

The anatomical location of imaging features is of crucial importance for...

Mohsen Ghafoorian, et al. ∙

research

∙ 01/15/2018

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...

Mohsen Ghafoorian, et al. ∙

research

∙ 02/19/2018

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...

Timothy de Moor, et al. ∙

research

∙ 04/16/2018

Training convolutional neural networks with megapixel images

To train deep convolutional neural networks, the input data and the inte...

Hans Pinckaers, et al. ∙

research

∙ 04/19/2018

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

We propose an unsupervised method using self-clustering convolutional ad...

Wouter Bulten, et al. ∙

research

∙ 05/23/2018

Detecting SNPs with interactive effects on a quantitative trait

Here we propose a test to detect effects of single nucleotide polymorphi...

Armin Rauschenberger, et al. ∙

research

∙ 08/14/2018

Deep Learning Framework for Digital Breast Tomosynthesis Reconstruction

Digital breast tomosynthesis is rapidly replacing digital mammography as...

Nikita Moriakov, et al. ∙

research

∙ 08/14/2018

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

Computer-aided detection aims to improve breast cancer screening program...

Joris van Vugt, et al. ∙

research

∙ 08/17/2018

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...

David Tellez, et al. ∙

research

∙ 08/29/2018

Chest X-ray Inpainting with Deep Generative Models

Generative adversarial networks have been successfully applied to inpain...

Ecem Sogancioglu, et al. ∙

research

∙ 09/13/2018

GANs for Medical Image Analysis

Generative Adversarial Networks (GANs) and their extensions have carved ...

Salome Kazeminia, et al. ∙

research

∙ 10/09/2018

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

Various approaches for liver segmentation in CT have been proposed: Besi...

Hans Meine, et al. ∙

research

∙ 12/03/2018

Chest X-Rays Image Inpainting with Context Encoders

Chest X-rays are one of the most commonly used technologies for medical ...

Davide Belli, et al. ∙

research

∙ 02/18/2019

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...

David Tellez, et al. ∙

research

∙ 05/16/2019

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...

Koen Dercksen, et al. ∙

research

∙ 07/02/2019

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 ...

Erdi Çallı, et al. ∙

research

∙ 02/12/2020

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

In a case-control study aimed at locating autosomal disease variants for...

Marianne A. Jonker, et al. ∙

research

∙ 03/13/2020

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

Random transformations are commonly used for augmentation of the trainin...

Nikolas Lessmann, et al. ∙

research

∙ 04/15/2020

Extending Unsupervised Neural Image Compression With Supervised Multitask Learning

We focus on the problem of training convolutional neural networks on gig...

David Tellez, et al. ∙

research

∙ 05/16/2020

Inferring astrophysical X-ray polarization with deep learning

We investigate the use of deep learning in the context of X-ray polariza...

Nikita Moriakov, et al. ∙

research

∙ 06/05/2020

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...

Hans Pinckaers, et al. ∙

research

∙ 06/11/2020

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...

Jonas Teuwen, et al. ∙

research

∙ 03/25/2021

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...

Erdi Çallı, et al. ∙

research

∙ 11/30/2021

Fully Automatic Deep Learning Framework for Pancreatic Ductal Adenocarcinoma Detection on Computed Tomography

Early detection improves prognosis in pancreatic ductal adenocarcinoma (...

Natália Alves, et al. ∙

research

∙ 05/27/2022

FlowNet-PET: Unsupervised Learning to Perform Respiratory Motion Correction in PET Imaging

To correct for breathing motion in PET imaging, an interpretable and uns...

Teaghan O'Briain, et al. ∙

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