Segmenting dynamic contrast enhanced-MRI series of small animal, which are intrinsically noisy and low contrasted images with low resolution, is the aim of this paper. To do this, a segmentation method taking into account the temporal (spectral) and
Multi-label problems arise in various domains such as multi-topic document categorization, pro- tein function prediction, and automatic image annotation. One natural way to deal with such problems is to construct a binary classifier for each label,
Most current work onclassification hasbeen focused on learningfrom a set of instances that are associated with a single label (i.e., single-label classi- fication). However, many applications, such as gene functional prediction and text categorizati
This article presents a Support Vector Machine (SVM) like learning sys- tem to handle multi-label problems. Such problems are usually decom- posed into many two-class problems but the expressive power of such a system can be weak [5, 7]. We explore
