activity
20182022
most citedA large annotated medical image dataset for the development and evaluation of segmentation algorithms

718 citations · 731 across the 5 of their papers we have counts for

collaborators

19 papers

cs.CV20224 cited

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…

eess.IV2021

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…

eess.IV2021

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…

eess.IV20212 cited

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…

eess.IV20207 cited

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…

eess.IV2020

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…