Sven Koitka
Sven Koitka
University Hospital Essen, Institute of Diagnostic and Interventional Radiology and Neuroradiology, Essen, Germany
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Cited by
Cited by
Utilizing neural networks and linguistic metadata for early detection of depression indications in text sequences
M Trotzek, S Koitka, CM Friedrich
IEEE Transactions on Knowledge and Data Engineering 32 (3), 588-601, 2018
Evaluation of combined artificial intelligence and radiologist assessment to interpret screening mammograms
T Schaffter, DSM Buist, CI Lee, Y Nikulin, D Ribli, Y Guan, W Lotter, Z Jie, ...
JAMA network open 3 (3), e200265-e200265, 2020
Traditional Feature Engineering and Deep Learning Approaches at Medical Classification Task of ImageCLEF 2016.
S Koitka, CM Friedrich
CLEF (Working Notes), 304-317, 2016
Radiology objects in context (ROCO): A multimodal image dataset
O Pelka, S Koitka, J Rückert, F Nensa, CM Friedrich
Intravascular Imaging and Computer Assisted Stenting and Large-Scale …, 2018
Linguistic Metadata Augmented Classifiers at the CLEF 2017 Task for Early Detection of Depression.
M Trotzek, S Koitka, CM Friedrich
CLEF (Working Notes), 2017
Word Embeddings and Linguistic Metadata at the CLEF 2018 Tasks for Early Detection of Depression and Anorexia.
M Trotzek, S Koitka, CM Friedrich
CLEF (Working Notes), 2018
Optimized convolutional neural network ensembles for medical subfigure classification
S Koitka, CM Friedrich
International Conference of the Cross-Language Evaluation Forum for European …, 2017
Ossification area localization in pediatric hand radiographs using deep neural networks for object detection
S Koitka, A Demircioglu, MS Kim, CM Friedrich, F Nensa
PloS one 13 (11), e0207496, 2018
Recognizing Bird Species in Audio Files Using Transfer Learning.
A Fritzler, S Koitka, CM Friedrich
CLEF (Working Notes), 2017
nmfgpu4R: GPU-Accelerated Computation of the Non-Negative Matrix Factorization (NMF) Using CUDA Capable Hardware.
S Koitka, CM Friedrich
R J. 8 (2), 382, 2016
Improving Model Performance for Plant Image Classification With Filtered Noisy Images.
AR Ludwig, H Piorek, AH Kelch, D Rex, S Koitka, CM Friedrich
CLEF (Working Notes), 2017
Early detection of depression based on linguistic metadata augmented classifiers revisited
M Trotzek, S Koitka, CM Friedrich
International Conference of the Cross-Language Evaluation Forum for European …, 2018
Fully-automated Body Composition Analysis in Routine CT Imaging Using 3D Semantic Segmentation Convolutional Neural Networks
S Koitka, L Kroll, E Malamutmann, A Oezcelik, F Nensa
European Radiology, doi 10.1007/s00330-020-07147-3, 2020
Differentiation Between Anteroposterior and Posteroanterior Chest X-Ray View Position With Convolutional Neural Networks
R Hosch, L Kroll, F Nensa, S Koitka
RöFo-Fortschritte auf dem Gebiet der Röntgenstrahlen und der bildgebenden …, 2021
Mimicking the radiologists’ workflow: Estimating pediatric hand bone age with stacked deep neural networks
S Koitka, MS Kim, M Qu, A Fischer, CM Friedrich, F Nensa
Medical Image Analysis 64, 101743, 2020
Big Imaging Data: Klinische Bildanalyse mit Radiomics und Deep Learning
A Demircioglu, S Koitka, F Nensa
Der Nuklearmediziner 42 (02), 97-111, 2019
Contrast agent dose reduction in computed tomography with deep learning using a conditional generative adversarial network
J Haubold, R Hosch, L Umutlu, A Wetter, P Haubold, A Radbruch, ...
European Radiology, 1-9, 2021
Assessing the Role of Pericardial Fat as a Biomarker Connected to Coronary Calcification—A Deep Learning Based Approach Using Fully Automated Body Composition Analysis
L Kroll, K Nassenstein, M Jochims, S Koitka, F Nensa
Journal of Clinical Medicine 10 (2), 356, 2021
Radiology Objects in Context (ROCO)
O Pelka, S Koitka, J Rückert, F Nensa, C Friedrich
Package ‘nmfgpu4R’
S Koitka, CM Friedrich, MS Koitka
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