15.4k citations · 21.2k across the 31 of their papers we have counts for
8 papers · 1 filter
Function Follows Form: Regression from Complete Thoracic Computed Tomography Scans
Max Argus, Cornelia Schaefer-Prokop, David A. Lynch +1
Chronic Obstructive Pulmonary Disease (COPD) is a leading cause of morbidity and mortality. While COPD diagnosis is based on lung function tests, early stages and progression of di…
mlVIRNET: Multilevel Variational Image Registration Network
Alessa Hering, Bram van Ginneken, Stefan Heldmann
We present a novel multilevel approach for deep learning based image registration. Recently published deep learning based registration methods have shown promising results for a wi…
A deep learning model for segmentation of geographic atrophy to study its long-term natural history
Bart Liefers, Johanna M. Colijn, Cristina González-Gonzalo +6
Purpose: To develop and validate a deep learning model for automatic segmentation of geographic atrophy (GA) in color fundus images (CFIs) and its application to study growth rate…
Chest CT Super-resolution and Domain-adaptation using Memory-efficient 3D Reversible GANs
Tycho F. A. van der Ouderaa, Daniel E. Worrall, Bram van Ginneken
Recently, paired (e.g. Pix2pix) and unpaired (e.g. CycleGAN) image-to-image translation methods have shown effective in medical imaging tasks. In practice, however, it can be diffi…
Automated interpretation of prenatal ultrasound using a predefined acquisition protocol in resource-limited countries
Thomas L. A. van den Heuvel, Chris L. de Korte, Bram van Ginneken
In this study, we combine a standardized acquisition protocol with image analysis algorithms to investigate if it is possible to automatically detect maternal risk factors without…
FRODO: Free rejection of out-of-distribution samples: application to chest x-ray analysis
Erdi Çallı, Keelin Murphy, Ecem Sogancioglu +1
In this work, we propose a method to reject out-of-distribution samples which can be adapted to any network architecture and requires no additional training data. Publicly availabl…
A large annotated medical image dataset for the development and evaluation of segmentation algorithms
Amber L. Simpson, Michela Antonelli, Spyridon Bakas +21
Semantic segmentation of medical images aims to associate a pixel with a label in a medical image without human initialization. The success of semantic segmentation algorithms is c…
The Liver Tumor Segmentation Benchmark (LiTS)
Patrick Bilic, Patrick Christ, Hongwei Bran Li +106
In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedi…