Automated left ventricle (LV) boundary delineation from left ventriculograms has been studied for decades. Unfortunately, no methods in terms of the accuracy about volume and ejection fraction have ever been reported. A new knowledge based multi-stage method to automatically delineate the LV boundary at end diastole and end systole is discussed in this paper. It has a mean absolute boundary err...

Dynamic Incentive Effects of Heterogeneity in Multi-Stage Promotion Contests This paper shows that the incentive effects of heterogeneity may be positive rather than negative in dynamic contests with multiple stages. In particular, the well-studied adverse effects of heterogeneity in static interactions are compensated by positive continuation-value and selection effects. Due to these positive ...

Modern methods for classification analysis involve processes for “learning” to correctly assign elements of a data set to certain classes. In many settings, the learning processes are supervised; i.e. the classes that the training data belong to are known in advance. In many other settings, however, the classes are not known a priori, and a process utilizing unsupervised learning is necessary. ...

Abstract Mental task classification (MTC), based on the electroencephalography (EEG) signals is a demanding brain–computer interface (BCI). It independent of all types muscular activity. MTC-based BCI systems are capable to identify cognitive activity human. The success system depends upon efficient feature representation from raw EEG for mental activities. This paper mainly presents novel (for...

In this paper we present a multi-stage classifier for magnetic resonance spectra of human brain tumours which is being developed as part of a decision support system for radiologists. The basic idea is to decompose a complex classification scheme into a sequence of classifiers, each specialising in different classes of tumours and trying to reproduce part of the WHO classification hierarchy. Ea...