Juan Ignacio Arribas

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We present a method for supervised, automatic, and reliable classification of healthy controls, patients with bipolar disorder, and patients with schizophrenia using brain imaging data. The method uses four supervised classification learning machines trained with a stochastic gradient learning rule based on the minimization of Kullback-Leibler divergence(More)
The problem of designing cost functions to estimate a posteriori probabilities in multiclass problems is addressed in this paper. We establish necessary and sufficient conditions that these costs must satisfy in one-class one-output networks whose outputs are consistent with probability laws. We focus our attention on a particular subset of the(More)
This paper proposes a novel algorithm to jointly determine the structure and the parameters of a posteriori probability model based on neural networks (NNs). It makes use of well-known ideas of pruning, splitting, and merging neural components and takes advantage of the probabilistic interpretation of these components. The algorithm, so called a posteriori(More)
An end-to-end system to automate the well-known Tanner--Whitehouse (TW3) clinical procedure to estimate the skeletal age in childhood is proposed. The system comprises the detailed analysis of the two most important bones in TW3: the radius and ulna wrist bones. First, a modified version of an adaptive clustering segmentation algorithm is presented to(More)
This paper1 presents an algorithm for automatically detecting bone contours from hand radiographs using active contours. Prior knowledge is first used to locate initial contours for the snakes inside each bone of interest. Next, an adaptive snake algorithm is applied so that parameters are properly adjusted for each bone specifically. We introduce a novel(More)
Recently, the concept of consistent image registration has been introduced to refer to a set of algorithms that estimate both the direct and inverse deformation together, that is, they exchange the roles of the target and the scene images alternatively; it has been demonstrated that this technique improves the registration accuracy, and that the biological(More)
In this paper, a new algorithm, the Joint Network and Data Density Estimation (XKDDE), is proposed to estimate the 'a posteriori' probabilities of the targets with neural networks in multiple classes problem. It is based on the estimation of conditional dens@ functions for each class with some restrictions or constraints imposed by the classifier structure(More)
Neural networks (NNs) are customarily used as classifiers aimed at minimizing classification error rates. However, it is known that the NN architectures that compute soft decisions can be used to estimate posterior class probabilities; sometimes, it could be useful to implement general decision rules other than the maximum a posteriori (MAP) decision(More)
Seizure-driven brain damage in epilepsy accumulates over time, especially in the hippocampus, which can lead to sclerosis, cognitive decline, and death. Excitotoxicity is the prevalent model to explain ictal neurodegeneration. Current labeling technologies cannot distinguish between excitotoxicity and hypoxia, however, because they share common molecular(More)