Otto-von-Guericke-Universität Magdeburg

 
 
 
 
 
 
 
 
Uli Niemann

M.Sc. Uli Niemann


Department of Technical and Business Information Systems (ITI)

Universitätsplatz 2, 39106, Magdeburg, Building 29-123

2017

Combining Subgroup Discovery and Clustering to Identify Diverse Subpopulations in Cohort Study Data. Proc. of the 30th IEEE Int. Symposium on Computer-Based Medical Systems (CBMS17), Thessaloniki, Greece, June 2017. accepted 04/2017.

ICE: Interactive Classification Rule Exploration on Epidemiological Data. Proc. of the 30th IEEE Int. Symposium on Computer-Based Medical Systems (CBMS17), Thessaloniki, Greece, June 2017. accepted 04/2017.

Classification of DCE-MRI Data for Breast Cancer Diagnosis Combining Contrast Agent Dynamics and Texture Features. Bildverarbeitung für die Medizin (BVM), 325-330, Springer Verlag, Heidelberg, 2017.

2016

Learning Pressure Patterns for Patients with Diabetic Foot Syndrome. Proc. of the 29th IEEE Int. Symposium on Computer-Based Medical Systems (CBMS16), IEEE, Dublin, Ireland and Belfast, Northern Ireland, June 2016.

Comparative Clustering of Plantar Pressure Distributions in Diabetics with Polyneuropathy May Be Applied to Reveal Inappropriate Biomechanical Stress. PLoS ONE, (11)8:1-12, Public Library of Science, August 2016. URL

2015

3D Regression Heat Map Analysis of Population Study Data. IEEE Transactions on Visualization and Computer Graphics (TVCG), (22)1:81-90, 2015.

Can we classify the participants of a longitudinal epidemiological study from their previous evolution?. Proc. of the 28th IEEE Int. Symposium on Computer-Based Medical Systems (CBMS15), 121-126, IEEE, São Carlos and Ribeirão Preto, Brazil, June 2015. URL

2014

Learning and inspecting classification rules from longitudinal epidemiological data to identify predictive features on hepatic steatosis. Expert Systems with Applications, (41)11:5405-5415, Elsevier BV, September 2014. URL

Subpopulation Discovery in Epidemiological Data with Subspace Clustering. Foundations of Computing and Decision Sciences (FCDS), (39)4:271-300, 2014. URL

Interactive Medical Miner: Interactively Exploring Subpopulations in Epidemiological Datasets. In Calders et al. (Eds.), European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2014) - DEMO TRACK, (8726):460-463, Springer Berlin Heidelberg, 2014. URL

2013

Classification of Benign and Malignant DCE-MRI Breast Tumors by Analyzing the Most Suspect Region. In Hans-Peter Meinzer, Thomas Martin Deserno, Heinz Handels, and Thomas Tolxdorff (Eds.), Bildverarbeitung für die Medizin 2013, 45-50, Springer Berlin Heidelberg, 2013. URL

Can we distinguish between benign and malignant breast tumors in DCE-MRI by studying a tumor's most suspect region only?. In Pedro Pereira Rodrigues, Mykola Pechenizkiy, João Gama, Ricardo Cruz-Correia, Jiming Liu, Agma J. M. Traina, Peter J. F. Lucas, and Paolo Soda (Eds.), CBMS, 77-82, IEEE, 2013. URL

 

 

 



Letzte Änderung: 20.03.2017 - Contact Person: M.Sc. Uli Niemann