Bram van Ginneken

research

∙ 07/13/2022

Domain adaptation strategies for cancer-independent detection of lymph node metastases

Recently, large, high-quality public datasets have led to the developmen...

Péter Bándi, 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

∙ 12/08/2021

Learn2Reg: comprehensive multi-task medical image registration challenge, dataset and evaluation in the era of deep learning

To date few studies have comprehensively compared medical image registra...

Alessa Hering, et al. ∙

research

∙ 09/30/2021

Robust Segmentation Models using an Uncertainty Slice Sampling Based Annotation Workflow

Semantic segmentation neural networks require pixel-level annotations in...

Grzegorz Chlebus, et al. ∙

research

∙ 06/01/2021

Deep Clustering Activation Maps for Emphysema Subtyping

We propose a deep learning clustering method that exploits dense feature...

Weiyi Xie, et al. ∙

research

∙ 05/25/2021

Dense Regression Activation Maps For Lesion Segmentation in CT scans of COVID-19 patients

Automatic lesion segmentation on thoracic CT enables rapid quantitative ...

Weiyi Xie, et al. ∙

research

∙ 05/03/2021

Automated Estimation of Total Lung Volume using Chest Radiographs and Deep Learning

Total lung volume is an important quantitative biomarker and is used for...

Ecem Sogancioglu, et al. ∙

research

∙ 04/12/2021

Common Limitations of Image Processing Metrics: A Picture Story

While the importance of automatic image analysis is increasing at an eno...

Annika Reinke, 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

∙ 03/15/2021

Deep Learning for Chest X-ray Analysis: A Survey

Recent advances in deep learning have led to a promising performance in ...

Ecem Sogancioglu, et al. ∙

research

∙ 01/16/2021

Adversarial cycle-consistent synthesis of cerebral microbleeds for data augmentation

We propose a novel framework for controllable pathological image synthes...

Khrystyna Faryna, et al. ∙

research

∙ 11/29/2020

Constraining Volume Change in Learned Image Registration for Lung CTs

Deep-learning-based registration methods emerged as a fast alternative t...

Alessa Hering, et al. ∙

research

∙ 09/23/2020

Anisotropic 3D Multi-Stream CNN for Accurate Prostate Segmentation from Multi-Planar MRI

Background and Objective: Accurate and reliable segmentation of the pros...

Anneke Meyer, et al. ∙

research

∙ 09/21/2020

Improving Automated COVID-19 Grading with Convolutional Neural Networks in Computed Tomography Scans: An Ablation Study

Amidst the ongoing pandemic, several studies have shown that COVID-19 cl...

Coen de Vente, 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

∙ 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

∙ 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

∙ 10/16/2019

Iterative augmentation of visual evidence for weakly-supervised lesion localization in deep interpretability frameworks

Interpretability of deep learning (DL) systems is gaining attention in m...

Cristina González-Gonzalo, et al. ∙

research

∙ 10/09/2019

BIAS: Transparent reporting of biomedical image analysis challenges

The number of biomedical image analysis challenges organized per year is...

Lena Maier-Hein, et al. ∙

research

∙ 09/26/2019

Function Follows Form: Regression from Complete Thoracic Computed Tomography Scans

Chronic Obstructive Pulmonary Disease (COPD) is a leading cause of morbi...

Max Argus, et al. ∙

research

∙ 09/26/2019

Follows Form: Regression from Complete Thoracic Computed Tomography Scans

Chronic Obstructive Pulmonary Disease (COPD) is a leading cause of morbi...

Max Argus, et al. ∙

research

∙ 09/22/2019

mlVIRNET: Multilevel Variational Image Registration Network

We present a novel multilevel approach for deep learning based image reg...

Alessa Hering, et al. ∙

research

∙ 08/15/2019

A deep learning model for segmentation of geographic atrophy to study its long-term natural history

Purpose: To develop and validate a deep learning model for automatic seg...

Bart Liefers, et al. ∙

research

∙ 07/18/2019

Automated Gleason Grading of Prostate Biopsies using Deep Learning

The Gleason score is the most important prognostic marker for prostate c...

Wouter Bulten, 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

∙ 03/22/2019

Evaluation of a deep learning system for the joint automated detection of diabetic retinopathy and age-related macular degeneration

Purpose: To validate the performance of a commercially-available, CE-cer...

Cristina González-Gonzalo, et al. ∙

research

∙ 03/08/2019

Computer aided detection of tuberculosis on chest radiographs: An evaluation of the CAD4TB v6 system

There is a growing interest in the automated analysis of chest X-Ray (CX...

Keelin Murphy, et al. ∙

research

∙ 02/25/2019

A large annotated medical image dataset for the development and evaluation of segmentation algorithms

Semantic segmentation of medical images aims to associate a pixel with a...

Amber L. Simpson, et al. ∙

research

∙ 01/13/2019

The Liver Tumor Segmentation Benchmark (LiTS)

In this work, we report the set-up and results of the Liver Tumor Segmen...

Patrick Bilic, 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

∙ 11/30/2018

iW-Net: an automatic and minimalistic interactive lung nodule segmentation deep network

We propose iW-Net, a deep learning model that allows for both automatic ...

Guilherme Aresta, 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

∙ 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

∙ 08/17/2018

Epithelium segmentation using deep learning in H&E-stained prostate specimens with immunohistochemistry as reference standard

Prostate cancer (PCa) is graded by pathologists by examining the archite...

Wouter Bulten, et al. ∙

research

∙ 06/06/2018

Is the winner really the best? A critical analysis of common research practice in biomedical image analysis competitions

International challenges have become the standard for validation of biom...

Lena Maier-Hein, et al. ∙

research

∙ 04/12/2018

Iterative fully convolutional neural networks for automatic vertebra segmentation

Precise segmentation of the vertebrae is often required for automatic de...

Nikolas Lessmann, 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

∙ 11/01/2017

Automatic calcium scoring in low-dose chest CT using deep neural networks with dilated convolutions

Heavy smokers undergoing screening with low-dose chest CT are affected b...

Nikolas Lessmann, 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

∙ 03/17/2017

Comparison of Different Methods for Tissue Segmentation in Histopathological Whole-Slide Images

Tissue segmentation is an important pre-requisite for efficient and accu...

Péter Bándi, 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

∙ 12/23/2016

Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: the LUNA16 challenge

Automatic detection of pulmonary nodules in thoracic computed tomography...

Arnaud Arindra Adiyoso Setio, et al. ∙

research

∙ 10/28/2016

Towards automatic pulmonary nodule management in lung cancer screening with deep learning

The introduction of lung cancer screening programs will produce an unpre...

Francesco Ciompi, 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. ∙

Bram van Ginneken

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