no code implementations • 15 Sep 2022 • Wouter A. C. van Amsterdam, Pim A. de Jong, Joost J. C. Verhoeff, Tim Leiner, Rajesh Ranganath
In cancer research there is much interest in building and validating outcome predicting outcomes to support treatment decisions.
no code implementations • 27 Aug 2020 • Ayoub Bagheri, T. Katrien J. Groenhof, Wouter B. Veldhuis, Pim A. de Jong, Folkert W. Asselbergs, Daniel L. Oberski
To exploit the potential information captured in EHRs, in this study we propose a multimodal recurrent neural network model for cardiovascular risk prediction that integrates both medical texts and structured clinical information.
1 code implementation • 30 Jun 2020 • Linde S. Hesse, Pim A. de Jong, Josien P. W. Pluim, Veronika Cheplygina
Therefore, we propose a detection and classification system for lung nodules in CT scans.
no code implementations • 12 Feb 2019 • Bob D. de Vos, Jelmer M. Wolterink, Tim Leiner, Pim A. de Jong, Nikolas Lessmann, Ivana Isgum
To meet demands of the increasing interest in quantification of CAC, i. e. coronary calcium scoring, especially as an unrequested finding for screening and research, automatic methods have been proposed.
no code implementations • 4 Oct 2018 • Sanne G. M. van Velzen, Majd Zreik, Nikolas Lessmann, Max A. Viergever, Pim A. de Jong, Helena M. Verkooijen, Ivana Išgum
Chest CT scans made in lung cancer screening are suitable for identification of participants at risk of CVD.
1 code implementation • 12 Apr 2018 • Nikolas Lessmann, Bram van Ginneken, Pim A. de Jong, Ivana Išgum
Precise segmentation and anatomical identification of the vertebrae provides the basis for automatic analysis of the spine, such as detection of vertebral compression fractures or other abnormalities.
no code implementations • 8 Dec 2017 • Bob D. de Vos, Nikolas Lessmann, Pim A. de Jong, Max A. Viergever, Ivana Isgum
The results demonstrate that real-time quantification of CAC burden in chest CT without the need for segmentation of CAC is possible.
no code implementations • 1 Nov 2017 • Nikolas Lessmann, Bram van Ginneken, Majd Zreik, Pim A. de Jong, Bob D. de Vos, Max A. Viergever, Ivana Išgum
On soft filter reconstructions, the method achieved F1 scores of 0. 89, 0. 89, 0. 67, and 0. 55 for coronary artery, thoracic aorta, aortic valve and mitral valve calcifications, respectively.
no code implementations • 19 Apr 2017 • Bob D. de Vos, Jelmer M. Wolterink, Pim A. de Jong, Tim Leiner, Max A. Viergever, Ivana Išgum
We propose a method for automatic localization of one or more anatomical structures in 3D medical images through detection of their presence in 2D image slices using a convolutional neural network (ConvNet).