handle: 2066/139156
Contains fulltext : 139156.pdf (Publisher’s version ) (Open Access) Inaugurele rede, 26 september 2013 22 p.
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handle: 2117/118400
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ispartof: Congress of the European Society of Biomechanics location:Patras, Greece date:25 Aug - 28 Aug 2013 status: published
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ispartof: M.M. Delacroix Workshop on Autism Research location:Leuven date:8 Nov - 10 Nov 2011 status: published
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handle: 20.500.12585/9955
The need to make timely and accurate diagnoses of brain diseases has posed challenges to computer-aided diagnosis systems. In this field, advances in deep learning techniques play an important role, as they carry out processes to extract relevant anatomical and functional characteristics of the tissues to classify them. In this paper, the study of various architectures of convolutional neural networks (CNN) is presented, with the aim of classifying three types of brain tumors in high-contrast magnetic resonance (MR) images. The architectures of the present study were VGG16, ResNet50, Xception, whose implementations are defined in the Keras framework. The evaluation of these architectures were preceded by data augmentation techniques and transfer learning, which improved the effectiveness of the training process, thanks to the use of pre-trained models with the ImageNet dataset. The VGG16 architecture was the one with the best performance, with an accuracy of 98.04%, followed by ResNet50 with 94.89%, and finally, Xception with 92.18%.
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ispartof: International Meeting for Autism Research location:London date:15 May - 17 May 2008 status: published
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ispartof: M.M. Delacroix Workshop on Autism Research location:Leuven date:8 Nov - 10 Nov 2011 status: published
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Humans can effortlessly extract the affective meaning of touch delivered to another person`s skin as well as their own. Interpersonal touch communication conveys discrete emotion and intention. For example, we can easily imagine experiencing a warm feeling when observing a person being hugged. Previous findings about simple touch observation suggested that this phenomenon could be linked to somatosensory resonance and the theory of mind (ToM). Yet, concerning more complex interpersonal affective touch, our understanding of how such mechanisms work is still limited. Thus, in the current study we generated a novel socio-affective touch database of 39 stimulus videos, covering both pleasant (e.g., hugging a person) and unpleasant (e.g. slapping a person) touch scenarios, and investigated how the human brain processes different types of interpersonal affective touch during passive observation. First, 21 participants evaluated pleasantness and arousal of each touch video. Subsequently, the same participants watched the same videos in the scanner. Importantly, we also provided the participants with both positive and negative touch stimulation in the scanner to capture actual touch sensitive cortices which we used as parts of regions of interest (ROI) along with social brain regions. Using correlational multivariate pattern analysis (MVPA) methods, neural spaces of affective touch were obtained in ROIs, followed by multiple regression analysis between the group neural matrix in each ROI and affective ratings. The results suggest that both actual touch sensitive cortices and social brain regions represent valence information after controlling the effects of arousal and other visual factors. Our findings highlight the involvement of social understanding and a mirror somatosensory system during observation of other`s affective touch interactions in the absence of actual touch. ispartof: International Association for the Study of Affective Touch location:Liverpool, UK date:1 Sep - 3 Sep 2017 status: published
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handle: 10641/1992
The design and development of computer assistive technologies must be tied to the needs and goals of end users and must take into account their capabilities and preferences. In this paper, we present MeDeC@, a Methodology for the Development of Computer Assistive Technologies for people with Autism Spectrum Disorders (ASD), which relies heavily in our experience working with end users with ASD. The aim of this methodology is not to design for a broad group of users, but to design highly customizable tools so that they can be easily adapted to specific situations and small user groups. We also present two applications developed using MeDeC@ in order to test its suitability: EmoTraductor, a web application for emotion recognition for people with Asperger Syndrome, and ReadIt, a web browser plug-in to help people with ASD with written language understanding difficulties to navigate the Internet. The results of our evaluation with end users show that the use of MeDeC@ helps developers to successfully design computer assistive technologies taking into account the special requirements and scenarios that arise when developing this kind of assistive applications. pre-print 703 KB
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handle: 2066/139156
Contains fulltext : 139156.pdf (Publisher’s version ) (Open Access) Inaugurele rede, 26 september 2013 22 p.
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handle: 2117/118400
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ispartof: Congress of the European Society of Biomechanics location:Patras, Greece date:25 Aug - 28 Aug 2013 status: published
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ispartof: M.M. Delacroix Workshop on Autism Research location:Leuven date:8 Nov - 10 Nov 2011 status: published
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handle: 20.500.12585/9955
The need to make timely and accurate diagnoses of brain diseases has posed challenges to computer-aided diagnosis systems. In this field, advances in deep learning techniques play an important role, as they carry out processes to extract relevant anatomical and functional characteristics of the tissues to classify them. In this paper, the study of various architectures of convolutional neural networks (CNN) is presented, with the aim of classifying three types of brain tumors in high-contrast magnetic resonance (MR) images. The architectures of the present study were VGG16, ResNet50, Xception, whose implementations are defined in the Keras framework. The evaluation of these architectures were preceded by data augmentation techniques and transfer learning, which improved the effectiveness of the training process, thanks to the use of pre-trained models with the ImageNet dataset. The VGG16 architecture was the one with the best performance, with an accuracy of 98.04%, followed by ResNet50 with 94.89%, and finally, Xception with 92.18%.
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