We have studied and applied various machine learning methods (e.g., CBR,SVM/SVR) in a number of clinical contexts, with one focus on time series analysis. More recently we have studied deep and convolutional neural networks for classification and regression tasks based on sensor and image data. Particularly, we extend deep learning methods to spatio-temporal data, including 4D image data. Application examples range from nephrology to tumor tissue detection to cardiology.
Selected publications
N. Gessert, M. Schlüter, A. Schlaefer (2018). A Deep Learning Approach for Pose Estimation from Volumetric OCT Data. Medical Image Analysis. 46 162-179.
N. Gessert, T. Priegnitz, T. Saathoff, S.-T. Antoni, D. Meyer, M. F. Hamann, K.-P. Jünemann, C. Otte, A. Schlaefer (2018). Needle Tip Force Estimation using an OCT Fiber and a Fused convGRU-CNN Architecture - MICCAI 2018. International Conference on Medical Image Computing and Computer-Assisted Intervention 222-229, Spotlight Talk.
C. Otte, S. Otte, L. Wittig, G. Hüttmann, C. Kugler, D. Drömann, A. Zell, A. Schlaefer (2014). Investigating Recurrent Neural Networks for OCT A-scan Based Tissue Analysis. Methods Inf Med. 53 (4), 245-249.
A. Schlaefer, S. Dieterich (2011). Feasibility of case-based beam generation for robotic radiosurgery. Artif Intell Med. 52 (2), 67-75.
L. Fritsche, A. Schlaefer, K. Budde, K. Schroeter, H. H. Neumayer (2002). Recognition of critical situations from time series of laboratory results by case-based reasoning. J Am Med Inform Assoc. 9 (5), 520-528.