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Ivan Gonzalez-Diaz

Ivan Gonzalez-Diaz received the Telecommunications Engineering degree from Universidad de Valladolid, Valladolid, Spain, in 1999, the M.Sc. and Ph.D. degree from Universidad Carlos III de Madrid, Madrid, Spain, in 2007 and 2011, respectively. After holding a postdoc position in the Laboratoire Bordelais de Recherche en Informatique at the University Bordeaux, he currently works as a Visiting Professor at the Signal Theory and Communications Department in Universidad Carlos III de Madrid. His primary research interests include object recognition, semantic image segmentation, scene understanding and content-based image and video retrieval systems. In these fields, he is coauthor of several papers in prestigious international journals, two chapters in international books and a few papers in revised international conferences.


Contributions

  • Position Paper Into the Wild: The Challenges of Physiological Stress Detection in Laboratory and Ambulatory Settings
    Vol. 23, Issue 2 DermaKNet: Incorporating the knowledge of dermatologists to Convolutional Neural Networks for skin lesion diagnosis

    Traditional approaches to automatic diagnosis of skin lesions consisted of classifiers working on sets of hand-crafted features, some of which modeled lesion aspects of special importance for dermatologists. Recently, the broad adoption of Convolutional Neural Networks(CNNs)in most computer vision tasks has brought about a great leap forward in terms of performance. Nevertheless, with this performance leap, the CNN-based Computer Aided Diagnosis (CAD) systems have also brought a notable reduction of the useful insights provided by hand-crafted features. This paper presents DermaKNet, a CAD system based on CNNs that incorporates specific subsystems modeling properties of skin lesions that are of special interest to dermatologists, aiming to improve the interpretability of its diagnosis. Our results prove that the incorporation of these subsystems not only improves the performance, but also enhances the diagnosis by providing more interpretable outputs.

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