Hierarchical Clustering for Image Classification in Dermatology: Towards Mobile Deploying.

In the context of increasing interest in computer-assisted diagnosis for skin lesion images and mobile applications to be used in real life settings, we propose a combined desktop-smartphone solution for dermatological image classification. Hierarchical agglomerative and divisive clustering are both implemented as methods of cluster analysis, with the RGB color histogram as descriptor for a global image analysis. The cosine similarity is employed for classifying the query image in one of the available clusters, characterized by their centroids.

Mobile technology support for clinical decision in diabetic keto-acidosis emergency.

The main challenge of effectively managing emergencies in diabetic ketoacidosis (DKA) is the fine tuning of the treatment in order to re-establish the normal metabolic homeostasis. We propose a mobile application for clinical decision support in DKA emergencies (mDKA), running under Android on smart phones and tablet PCs. mDKA provides decision support for treatment concerning the main components (i.

A review of ensemble methods for de novo motif discovery in ChIP-Seq data.

De novo motif discovery is a difficult computational task. Historically, dedicated algorithms always reported a high percentage of false positives. Their performance did not improve considerably even after they adapted to handle large amounts of chromatin immunoprecipitation sequencing (ChIP-Seq) data.