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Mireia Forns-Nadal; Federico Sem; Anna Mane; Laura Igual; Dani Guinart; Oscar Vilarroya |
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Increased Nucleus Accumbens Volume in First-Episode Psychosis |
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Journal Article |
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2017 |
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Psychiatry Research-Neuroimaging |
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PRN |
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263 |
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57-60 |
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Nucleus accumbens has been reported as a key structure in the neurobiology of schizophrenia. Studies analyzing structural abnormalities have shown conflicting results, possibly related to confounding factors. We investigated the nucleus accumbens volume using manual delimitation in first-episode psychosis (FEP) controlling for age, cannabis use and medication. Thirty-one FEP subjects who were naive or minimally exposed to antipsychotics and a control group were MRI scanned and clinically assessed from baseline to 6 months of follow-up. FEP showed increased relative and total accumbens volumes. Clinical correlations with negative symptoms, duration of untreated psychosis and cannabis use were not significant. |
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MILAB; no menciona |
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no |
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Admin @ si @ FSM2017 |
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3028 |
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Marc Bolaños; Alvaro Peris; Francisco Casacuberta; Sergi Solera; Petia Radeva |
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Egocentric video description based on temporally-linked sequences |
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Journal Article |
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2018 |
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Journal of Visual Communication and Image Representation |
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JVCIR |
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50 |
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205-216 |
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egocentric vision; video description; deep learning; multi-modal learning |
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Egocentric vision consists in acquiring images along the day from a first person point-of-view using wearable cameras. The automatic analysis of this information allows to discover daily patterns for improving the quality of life of the user. A natural topic that arises in egocentric vision is storytelling, that is, how to understand and tell the story relying behind the pictures.
In this paper, we tackle storytelling as an egocentric sequences description problem. We propose a novel methodology that exploits information from temporally neighboring events, matching precisely the nature of egocentric sequences. Furthermore, we present a new method for multimodal data fusion consisting on a multi-input attention recurrent network. We also release the EDUB-SegDesc dataset. This is the first dataset for egocentric image sequences description, consisting of 1,339 events with 3,991 descriptions, from 55 days acquired by 11 people. Finally, we prove that our proposal outperforms classical attentional encoder-decoder methods for video description. |
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MILAB; no proj |
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Admin @ si @ BPC2018 |
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3109 |
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Alejandro Cartas; Juan Marin; Petia Radeva; Mariella Dimiccoli |
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Title |
Batch-based activity recognition from egocentric photo-streams revisited |
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2018 |
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Pattern Analysis and Applications |
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PAA |
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21 |
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4 |
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953–965 |
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Egocentric vision; Lifelogging; Activity recognition; Deep learning; Recurrent neural networks |
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Wearable cameras can gather large amounts of image data that provide rich visual information about the daily activities of the wearer. Motivated by the large number of health applications that could be enabled by the automatic recognition of daily activities, such as lifestyle characterization for habit improvement, context-aware personal assistance and tele-rehabilitation services, we propose a system to classify 21 daily activities from photo-streams acquired by a wearable photo-camera. Our approach combines the advantages of a late fusion ensemble strategy relying on convolutional neural networks at image level with the ability of recurrent neural networks to account for the temporal evolution of high-level features in photo-streams without relying on event boundaries. The proposed batch-based approach achieved an overall accuracy of 89.85%, outperforming state-of-the-art end-to-end methodologies. These results were achieved on a dataset consists of 44,902 egocentric pictures from three persons captured during 26 days in average. |
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MILAB; no proj |
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no |
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Admin @ si @ CMR2018 |
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3186 |
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Mariella Dimiccoli; Cathal Gurrin; David J. Crandall; Xavier Giro; Petia Radeva |
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Introduction to the special issue: Egocentric Vision and Lifelogging |
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2018 |
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Journal of Visual Communication and Image Representation |
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JVCIR |
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55 |
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352-353 |
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MILAB; no proj |
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no |
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Admin @ si @ DGC2018 |
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3187 |
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Sumit K. Banchhor; Narendra D. Londhe; Tadashi Araki; Luca Saba; Petia Radeva; Narendra N. Khanna; Jasjit S. Suri |
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Calcium detection, its quantification, and grayscale morphology-based risk stratification using machine learning in multimodality big data coronary and carotid scans: A review. |
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Journal Article |
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2018 |
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Computers in Biology and Medicine |
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CBM |
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101 |
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184-198 |
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Heart disease; Stroke; Atherosclerosis; Intravascular; Coronary; Carotid; Calcium; Morphology; Risk stratification |
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Purpose of review
Atherosclerosis is the leading cause of cardiovascular disease (CVD) and stroke. Typically, atherosclerotic calcium is found during the mature stage of the atherosclerosis disease. It is therefore often a challenge to identify and quantify the calcium. This is due to the presence of multiple components of plaque buildup in the arterial walls. The American College of Cardiology/American Heart Association guidelines point to the importance of calcium in the coronary and carotid arteries and further recommend its quantification for the prevention of heart disease. It is therefore essential to stratify the CVD risk of the patient into low- and high-risk bins.
Recent finding
Calcium formation in the artery walls is multifocal in nature with sizes at the micrometer level. Thus, its detection requires high-resolution imaging. Clinical experience has shown that even though optical coherence tomography offers better resolution, intravascular ultrasound still remains an important imaging modality for coronary wall imaging. For a computer-based analysis system to be complete, it must be scientifically and clinically validated. This study presents a state-of-the-art review (condensation of 152 publications after examining 200 articles) covering the methods for calcium detection and its quantification for coronary and carotid arteries, the pros and cons of these methods, and the risk stratification strategies. The review also presents different kinds of statistical models and gold standard solutions for the evaluation of software systems useful for calcium detection and quantification. Finally, the review concludes with a possible vision for designing the next-generation system for better clinical outcomes. |
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MILAB; no proj |
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no |
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Admin @ si @ BLA2018 |
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3188 |
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