15.4k citations · 21.2k across the 31 of their papers we have counts for
32 papers
Designing UNICORN: a Unified Benchmark for Imaging in Computational Pathology, Radiology, and Natural Language
Michelle Stegeman, Lena Philipp, Fennie van der Graaf +20
Foundation models are changing the way we develop medical artificial intelligence. By learning broadly generalizable features across diverse data modalities, a single model can be…
Divide to Conquer: A Field Decomposition Approach for Multi-Organ Whole-Body CT Image Registration
Xuan Loc Pham, Mathias Prokop, Bram van Ginneken +1
Image registration is an essential technique for the analysis of Computed Tomography (CT) images in clinical practice. However, existing methodologies are predominantly tailored to…
In the Picture: Medical Imaging Datasets, Artifacts, and their Living Review
Amelia Jiménez-Sánchez, Natalia-Rozalia Avlona, Sarah de Boer +26
Datasets play a critical role in medical imaging research, yet issues such as label quality, shortcuts, and metadata are often overlooked. This lack of attention may harm the gener…
World of Forms: Deformable Geometric Templates for One-Shot Surface Meshing in Coronary CT Angiography
Rudolf L. M. van Herten, Ioannis Lagogiannis, Jelmer M. Wolterink +8
Deep learning-based medical image segmentation and surface mesh generation typically involve a sequential pipeline from image to segmentation to meshes, often requiring large train…
Nodule detection and generation on chest X-rays: NODE21 Challenge
Ecem Sogancioglu, Bram van Ginneken, Finn Behrendt +16
Pulmonary nodules may be an early manifestation of lung cancer, the leading cause of cancer-related deaths among both men and women. Numerous studies have established that deep lea…
The STOIC2021 COVID-19 AI challenge: applying reusable training methodologies to private data
Luuk H. Boulogne, Julian Lorenz, Daniel Kienzle +35
Challenges drive the state-of-the-art of automated medical image analysis. The quantity of public training data that they provide can limit the performance of their solutions. Publ…
AIROGS: Artificial Intelligence for RObust Glaucoma Screening Challenge
Coen de Vente, Koenraad A. Vermeer, Nicolas Jaccard +33
The early detection of glaucoma is essential in preventing visual impairment. Artificial intelligence (AI) can be used to analyze color fundus photographs (CFPs) in a cost-effectiv…
Structure and position-aware graph neural network for airway labeling
Weiyi Xie, Colin Jacobs, Jean-Paul Charbonnier +1
We present a novel graph-based approach for labeling the anatomical branches of a given airway tree segmentation. The proposed method formulates airway labeling as a branch classif…
The Medical Segmentation Decathlon
Michela Antonelli, Annika Reinke, Spyridon Bakas +55
International challenges have become the de facto standard for comparative assessment of image analysis algorithms given a specific task. Segmentation is so far the most widely inv…
Common Limitations of Image Processing Metrics: A Picture Story
Annika Reinke, Minu D. Tizabi, Carole H. Sudre +90
While the importance of automatic image analysis is continuously increasing, recent meta-research revealed major flaws with respect to algorithm validation. Performance metrics are…
Deep Learning for Chest X-ray Analysis: A Survey
Ecem Sogancioglu, Erdi Çallı, Bram van Ginneken +2
Recent advances in deep learning have led to a promising performance in many medical image analysis tasks. As the most commonly performed radiological exam, chest radiographs are a…
Deep Learning with robustness to missing data: A novel approach to the detection of COVID-19
Erdi Çallı, Keelin Murphy, Steef Kurstjens +5
In the context of the current global pandemic and the limitations of the RT-PCR test, we propose a novel deep learning architecture, DFCN (Denoising Fully Connected Network). Since…
Adversarial cycle-consistent synthesis of cerebral microbleeds for data augmentation
Khrystyna Faryna, Kevin Koschmieder, Marcella M. Paul +4
We propose a novel framework for controllable pathological image synthesis for data augmentation. Inspired by CycleGAN, we perform cycle-consistent image-to-image translation betwe…
Improving Automated COVID-19 Grading with Convolutional Neural Networks in Computed Tomography Scans: An Ablation Study
Coen de Vente, Luuk H. Boulogne, Kiran Vaidhya Venkadesh +5
Amidst the ongoing pandemic, several studies have shown that COVID-19 classification and grading using computed tomography (CT) images can be automated with convolutional neural ne…
Anisotropic 3D Multi-Stream CNN for Accurate Prostate Segmentation from Multi-Planar MRI
Anneke Meyer, Grzegorz Chlebus, Marko Rak +8
Background and Objective: Accurate and reliable segmentation of the prostate gland in MR images can support the clinical assessment of prostate cancer, as well as the planning and…
A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises
S. Kevin Zhou, Hayit Greenspan, Christos Davatzikos +6
Since its renaissance, deep learning has been widely used in various medical imaging tasks and has achieved remarkable success in many medical imaging applications, thereby propell…
Random smooth gray value transformations for cross modality learning with gray value invariant networks
Nikolas Lessmann, Bram van Ginneken
Random transformations are commonly used for augmentation of the training data with the goal of reducing the uniformity of the training samples. These transformations normally aim…
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…