Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
A deterministic annealing approach to clustering
Pattern Recognition Letters
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Topology representing networks
Neural Networks
Machine Learning
Segmentation with neural networks
Handbook of medical imaging
Generative probability density model in the self-organizing map
Self-Organizing neural networks
Artificial Neural Networks in Medicine and Biology: Proceedings of the Annimab-1 Conference, Goteborg, Sweden, 13-16 May 2000
Cluster Analysis of Biomedical Image Time-Series
International Journal of Computer Vision
Clustering based on conditional distributions in an auxiliary space
Neural Computation
Neural Network Analysis of Dynamic Contrast-Enhanced MRI Mammography
ICANN '01 Proceedings of the International Conference on Artificial Neural Networks
Clustering Gene Expression Data by Mutual Information with Gene Function
ICANN '01 Proceedings of the International Conference on Artificial Neural Networks
Model-free functional MRI analysis based on unsupervised clustering
Journal of Biomedical Informatics
Fully automated biomedical image segmentation by self-organized model adaptation
Neural Networks - 2004 Special issue: New developments in self-organizing systems
Fast learning in networks of locally-tuned processing units
Neural Computation
Controlling the magnification factor of self-organizing feature maps
Neural Computation
MICCAI '08 Proceedings of the 11th International Conference on Medical Image Computing and Computer-Assisted Intervention, Part II
A Computational Framework for Nonlinear Dimensionality Reduction and Clustering
WSOM '09 Proceedings of the 7th International Workshop on Advances in Self-Organizing Maps
The Exploration Machine --- A Novel Method for Data Visualization
WSOM '09 Proceedings of the 7th International Workshop on Advances in Self-Organizing Maps
Journal of Biomedical Imaging
IEEE Transactions on Information Technology in Biomedicine
Topology preservation in self-organizing feature maps: exact definition and measurement
IEEE Transactions on Neural Networks
Clustering of the self-organizing map
IEEE Transactions on Neural Networks
A new model of self-organizing neural networks and its application in data projection
IEEE Transactions on Neural Networks
Asymptotic level density in topological feature maps
IEEE Transactions on Neural Networks
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Technical innovations in radiology, such as advanced cross-sectional imaging methods, have opened up new vistas for the exploration of structure and function of the human body enabling both high spatial and temporal resolution. However, these techniques have led to vast amounts of data whose precise and reliable visual analysis by radiologists requires a considerable amount of human intervention and expertise, thus resulting in a cost factor of substantial economic relevance. Hence, the computer-assisted analysis of biomedical image data has moved into the focus of interest as an issue of high priority research efforts. In this context, innovative approaches to exploratory analysis of huge complex spatio-temporal patterns play a key role to improve computer-assisted signal and image processing in radiology. Examples of such approaches are various unsupervised vector quantization methods or supervised function approximation techniques, such as Generalized Radial-Basis-Functions- (GRBF-) neural networks. Recent developments motivated by concepts of computational intelligence are the `Deformable Feature Map' (DM) as an algorithm for self-organized model adaptation, the `Mutual Connectivity Analysis' (MCA) as an instrument for the analysis of large time-series ensembles and the `Exploratory Observation Machine' (XOM) as a novel general framework for learning by self-organization--three methods that the author has invented and applied to biomedical real-world applications. This contribution covers both conceptual foundations and applications of such methods for pattern recognition and analysis to a wide scope of radiological data sets, such as structural and functional segmentation in Magnetic Resonance Imaging (MRI), ranging from functional MRI for human brain mapping to the monitoring of disease progression in multiple sclerosis by automatic lesion segmentation, as well as novel approaches to image time-series analysis in MRI mammography for breast cancer diagnosis. Current projects related to the modeling of speech production and to genome-wide expression analysis of microarray data in bioinformatics confirm the broad applicability of the presented methods.