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Sven Koitka

A Fully Automated Machine Learning Approach for Predicting Contrast Phases in CT Imaging
A CT-Based Body and Organ Analysis for Radiologists at the Point of Care
Optimizing Platelet Transfusion through a Personalized Deep Learning Risk Assessment System for Demand Management
FHIR-PYrate: a data science friendly Python package to query FHIR servers
Predicting Individual Patient Platelet Demand in a Large Tertiary Care Hospital Using Machine Learning
Contrast agent dose reduction in computed tomography with deep learning using a conditional generative adversarial network.
Assessing the Role of Pericardial Fat as a Biomarker Connected to Coronary Calcification-A Deep Learning Based Approach Using Fully Automated Body Composition Analysis.
Fully automated body composition analysis in routine CT imaging using 3D semantic segmentation convolutional neural networks.
Differentiation Between Anteroposterior and Posteroanterior Chest X-Ray View Position With Convolutional Neural Networks.
Mimicking the radiologists' workflow: Estimating pediatric hand bone age with stacked deep neural networks.
Evaluation of Combined Artificial Intelligence and Radiologist Assessment to Interpret Screening Mammograms.
Ossification area localization in pediatric hand radiographs using deep neural networks for object detection.

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