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» Two Methods for Validating Brain Tissue Classifiers
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ISBI
2011
IEEE
12 years 11 months ago
Trace driven registration of neuron confocal microscopy stacks
Active research in the area of 3-D neurite tracing has predominantly focused on single sections. Ultimately, however, neurobiologists desire to study the long range connectivity o...
Luke Hogrebe, António R. C. Paiva, Elizabet...
TMI
2010
175views more  TMI 2010»
13 years 2 months ago
Spatially Adaptive Mixture Modeling for Analysis of fMRI Time Series
Within-subject analysis in fMRI essentially addresses two problems, the detection of brain regions eliciting evoked activity and the estimation of the underlying dynamics. In [1, 2...
Thomas Vincent, Laurent Risser, Philippe Ciuciu
CGF
2008
141views more  CGF 2008»
13 years 7 months ago
Interactive Visualization of Multimodal Volume Data for Neurosurgical Tumor Treatment
Teaser Figure: Left: Brain, visualized using silhouettes, the lesion's spatial depth is displayed using a ring. Center: Combined rendering of brain tissue, skull and fiber tr...
Christian Rieder, Felix Ritter, Matthias Raspe, He...
MCS
2010
Springer
13 years 9 months ago
Dynamic Selection of Ensembles of Classifiers Using Contextual Information
In a multiple classifier system, dynamic selection (DS) has been used successfully to choose only the best subset of classifiers to recognize the test samples. Dos Santos et al...
Paulo Rodrigo Cavalin, Robert Sabourin, Ching Y. S...
PR
2008
154views more  PR 2008»
13 years 7 months ago
Data-driven decomposition for multi-class classification
This paper presents a new study on a method of designing a multi-class classifier: Data-driven Error Correcting Output Coding (DECOC). DECOC is based on the principle of Error Cor...
Jie Zhou, Hanchuan Peng, Ching Y. Suen