Effects of Variability in Synthetic Training Data on Convolutional Neural Networks for 3D Head Reconstruction

Göpfert JP, Göpfert C, Botsch M, Hammer B. Effects of Variability in Synthetic Training Data on Convolutional Neural Networks for 3D Head Reconstruction. In: 2017 SSCI Proceedings. 2017 IEEE Symposium Series on Computational Intelligence (SSCI). Piscataway, NJ: IEEE; 2017

Analyzing Feature Relevance for Linear Reject Option SVM using Relevance Intervals

Göpfert C, Göpfert JP, Hammer B. Analyzing Feature Relevance for Linear Reject Option SVM using Relevance Intervals. In: Proceedings of the 2017 NIPS workshop on Transparent and Interpretable Machine Learning in Safety Critical Environments. 2017

Interpretation of Linear Classifiers by Means of Feature Relevance Bounds

Göpfert C, Pfannschmidt L, Göpfert JP, Hammer B. Interpretation of Linear Classifiers by Means of Feature Relevance Bounds. Neurocomputing. 2018;298:69-79.Research on feature relevance and feature selection problems goes back several decades, but the importance of these areas continues to grow as more and more data becomes available, and machine learning methods are used to gain insight and interpret, rather than solely to solve classification or regression problems. Despite the fact that feature relevance is often discussed, it is frequently poorly defined, and the feature selection problems studied are subtly different. Furthermore, the problem of finding all features relevant for a classification problem has only recently started to gain traction, despite its importance for interpretability and integrating expert knowledge. In this paper, we attempt to unify commonly used concepts and to give an overview of the main questions and results. We formalize two interpretations of the all-relevant problem and propose a polynomial method to approximate one of them for the important hypothesis class of linear classifiers, which also enables a distinction between strongly and weakly relevant features

Mitigating Concept Drift via Rejection

Göpfert JP, Hammer B, Wersing H. Mitigating Concept Drift via Rejection. In: Kurkova V, Manolopoulos Y, Hammer B, Iliadis L, Maglogiannis I, eds. Artificial Neural Networks and Machine Learning – ICANN 2018. Proceedings, Part I. Lecture Notes in Computer Science. Vol 11139. Cham: Springer; 2018

SCIE: Information Extraction for Spinal Cord Injury Preclinical Experiments – A Webservice and Open Source Toolkit

Stöckel A, Paaßen B, Dickfelder R, et al. SCIE: Information Extraction for Spinal Cord Injury Preclinical Experiments – A Webservice and Open Source Toolkit. bioRxive.org; 2015.Translational neuroscience in the field of spinal cord injuries (SCI) faces a strong disproportion between immense preclinical research efforts and a lack of therapeutic approaches success- ful in human patients: Currently, preclinical research on SCI yields more than 3,000 new publications per year (8,000 when including the whole central nervous system, growing at an exponential rate), whereas none of the result- ing therapeutic concepts has led to functional recovery of neural tissue in humans. Improving clinical researchers’ information access there- fore carries the potential to support more effec- tive selection of promising therapy candidates from preclinical studies. Thus, automated in- formation extraction from scientific publica- tions contributes to enabling meta studies and therapy grading by aggregating relevant infor- mation from the entire body of previous work on SCI. We present SCIE, an automated information extraction pipeline capable of detecting rele- vant information in SCI publications based on ontological entity and probabilistic relation de- tection. The input are plain text or PDF doc- uments. As output, the user choses between an online visualization or a machine-readable format. Compared to human gold standard annotations, our system achieves an average extraction performance of 76 % precision and 52 % recall (F1-measure 0.59). An instance of the webservice is available at http://scie.sc.cit-ec.uni-bielefeld.de/. SCIE is free software licensed under the AGPL and can be downloaded for local installation at http: //opensource.cit-ec.de/projects/scie/

Ontology-based Extraction of Structured Information from Publications on Preclinical Experiments for Spinal Cord Injury Treatments

Paaßen B, Stöckel A, Dickfelder R, et al. Ontology-based Extraction of Structured Information from Publications on Preclinical Experiments for Spinal Cord Injury Treatments. In: Maynard D, Erp van M, Davis B, eds. Third Workshop on Semantic Web and Information Extraction (SWAIE). The 25th International Conference on Computational Linguistics (COLING). Dublin, Ireland; 2014: 25-32.Preclinical research in the field of central nervous system trauma advances at a fast pace, currently yielding over 8,000 new publications per year, at an exponentially growing rate. This amount of published information by far exceeds the capacity of individual scientists to read and understand the relevant literature. So far, no clinical trial has led to therapeutic approaches which achieve functional recovery in human patients. In this paper, we describe a first prototype of an ontology-based information extraction system that automatically extracts relevant preclinical knowledge about spinal cord injury treatments from natural language text by recognizing participating entity classes and linking them to each other. The evaluation on an independent test corpus of manually annotated full text articles shows a macro-average F1 measure of 0.74 with precision 0.68 and recall 0.81 on the task of identifying entities participating in relations

Deep learning for understanding satellite imagery : an experimental survey

Translating satellite imagery into maps requires intensive effort and time, especially leading to inaccurate maps of the affected regions during disaster and conflict. The combination of availability of recent datasets and advances in computer vision made through deep learning paved the way toward automated satellite image translation. To facilitate research in this direction, we introduce the Satellite Imagery Competition using a modified SpaceNet dataset. Participants had to come up with different segmentation models to detect positions of buildings on satellite images. In this work, we present five approaches based on improvements of U-Net and Mask R-Convolutional Neuronal Networks models, coupled with unique training adaptations using boosting algorithms, morphological filter, Conditional Random Fields and custom losses. The good results-as high as AP=0.937 and AR=0.959 -from these models demonstrate the feasibility of Deep Learning in automated satellite image annotation