activity
20172020
most citedIterative fully convolutional neural networks for automatic vertebra segmentation and identification

270 citations · 271 across the 4 of their papers we have counts for

collaborators

5 papers

cs.LG2020

Multimodal Learning for Cardiovascular Risk Prediction using EHR Data

Ayoub Bagheri, T. Katrien J. Groenhof, Wouter B. Veldhuis +3

Electronic health records (EHRs) contain structured and unstructured data of significant clinical and research value. Various machine learning approaches have been developed to emp…

cs.CV2020

Primary Tumor Origin Classification of Lung Nodules in Spectral CT using Transfer Learning

Linde S. Hesse, Pim A. de Jong, Josien P. W. Pluim +1

Early detection of lung cancer has been proven to decrease mortality significantly. A recent development in computed tomography (CT), spectral CT, can potentially improve diagnosti…

cs.CV2018

Direct Prediction of Cardiovascular Mortality from Low-dose Chest CT using Deep Learning

Sanne G. M. van Velzen, Majd Zreik, Nikolas Lessmann +4

Cardiovascular disease (CVD) is a leading cause of death in the lung cancer screening population. Chest CT scans made in lung cancer screening are suitable for identification of pa…

cs.CV2018★ 270 cited

Iterative fully convolutional neural networks for automatic vertebra segmentation and identification

Nikolas Lessmann, Bram van Ginneken, Pim A. de Jong +1

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…

cs.CV2017★ 1 cited

Direct and Real-Time Cardiovascular Risk Prediction

Bob D. de Vos, Nikolas Lessmann, Pim A. de Jong +2

Coronary artery calcium (CAC) burden quantified in low-dose chest CT is a predictor of cardiovascular events. We propose an automatic method for CAC quantification, circumventing i…