Search Results for author:
Found 9 papers, 2 papers with code
From algorithms to action: improving patient care requires causality
In cancer research there is much interest in building and validating outcome predicting outcomes to support treatment decisions.
Multimodal Learning for Cardiovascular Risk Prediction using EHR Data
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.
Primary Tumor Origin Classification of Lung Nodules in Spectral CT using Transfer Learning
Therefore, we propose a detection and classification system for lung nodules in CT scans.
Direct Automatic Coronary Calcium Scoring in Cardiac and Chest CT
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.
Direct Prediction of Cardiovascular Mortality from Low-dose Chest CT using Deep Learning
Chest CT scans made in lung cancer screening are suitable for identification of participants at risk of CVD.
Iterative fully convolutional neural networks for automatic vertebra segmentation and identification
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.
Direct and Real-Time Cardiovascular Risk Prediction
The results demonstrate that real-time quantification of CAC burden in chest CT without the need for segmentation of CAC is possible.
Automatic calcium scoring in low-dose chest CT using deep neural networks with dilated convolutions
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.
ConvNet-Based Localization of Anatomical Structures in 3D Medical Images
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).
