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Author Xavier Soria; Angel Sappa; Riad I. Hammoud
Title Wide-Band Color Imagery Restoration for RGB-NIR Single Sensor Images Type Journal Article
Year 2018 Publication Sensors Abbreviated Journal SENS
Volume 18 Issue (down) 7 Pages 2059
Keywords RGB-NIR sensor; multispectral imaging; deep learning; CNNs
Abstract Multi-spectral RGB-NIR sensors have become ubiquitous in recent years. These sensors allow the visible and near-infrared spectral bands of a given scene to be captured at the same time. With such cameras, the acquired imagery has a compromised RGB color representation due to near-infrared bands (700–1100 nm) cross-talking with the visible bands (400–700 nm).
This paper proposes two deep learning-based architectures to recover the full RGB color images, thus removing the NIR information from the visible bands. The proposed approaches directly restore the high-resolution RGB image by means of convolutional neural networks. They are evaluated with several outdoor images; both architectures reach a similar performance when evaluated in different
scenarios and using different similarity metrics. Both of them improve the state of the art approaches.
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Notes ADAS; MSIAU; 600.086; 600.130; 600.122; 600.118 Approved no
Call Number Admin @ si @ SSH2018 Serial 3145
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Author Diana Ramirez Cifuentes; Ana Freire; Ricardo Baeza Yates; Joaquim Punti Vidal; Pilar Medina Bravo; Diego Velazquez; Josep M. Gonfaus; Jordi Gonzalez
Title Detection of Suicidal Ideation on Social Media: Multimodal, Relational, and Behavioral Analysis Type Journal Article
Year 2020 Publication Journal of Medical Internet Research Abbreviated Journal JMIR
Volume 22 Issue (down) 7 Pages e17758
Keywords
Abstract Background:
Suicide risk assessment usually involves an interaction between doctors and patients. However, a significant number of people with mental disorders receive no treatment for their condition due to the limited access to mental health care facilities; the reduced availability of clinicians; the lack of awareness; and stigma, neglect, and discrimination surrounding mental disorders. In contrast, internet access and social media usage have increased significantly, providing experts and patients with a means of communication that may contribute to the development of methods to detect mental health issues among social media users.

Objective:
This paper aimed to describe an approach for the suicide risk assessment of Spanish-speaking users on social media. We aimed to explore behavioral, relational, and multimodal data extracted from multiple social platforms and develop machine learning models to detect users at risk.

Methods:
We characterized users based on their writings, posting patterns, relations with other users, and images posted. We also evaluated statistical and deep learning approaches to handle multimodal data for the detection of users with signs of suicidal ideation (suicidal ideation risk group). Our methods were evaluated over a dataset of 252 users annotated by clinicians. To evaluate the performance of our models, we distinguished 2 control groups: users who make use of suicide-related vocabulary (focused control group) and generic random users (generic control group).

Results:
We identified significant statistical differences between the textual and behavioral attributes of each of the control groups compared with the suicidal ideation risk group. At a 95% CI, when comparing the suicidal ideation risk group and the focused control group, the number of friends (P=.04) and median tweet length (P=.04) were significantly different. The median number of friends for a focused control user (median 578.5) was higher than that for a user at risk (median 372.0). Similarly, the median tweet length was higher for focused control users, with 16 words against 13 words of suicidal ideation risk users. Our findings also show that the combination of textual, visual, relational, and behavioral data outperforms the accuracy of using each modality separately. We defined text-based baseline models based on bag of words and word embeddings, which were outperformed by our models, obtaining an increase in accuracy of up to 8% when distinguishing users at risk from both types of control users.

Conclusions:
The types of attributes analyzed are significant for detecting users at risk, and their combination outperforms the results provided by generic, exclusively text-based baseline models. After evaluating the contribution of image-based predictive models, we believe that our results can be improved by enhancing the models based on textual and relational features. These methods can be extended and applied to different use cases related to other mental disorders.
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Notes ISE; 600.098; 600.119 Approved no
Call Number Admin @ si @ RFB2020 Serial 3552
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Author Diana Ramirez Cifuentes; Ana Freire; Ricardo Baeza Yates; Nadia Sanz Lamora; Aida Alvarez; Alexandre Gonzalez; Meritxell Lozano; Roger Llobet; Diego Velazquez; Josep M. Gonfaus; Jordi Gonzalez
Title Characterization of Anorexia Nervosa on Social Media: Textual, Visual, Relational, Behavioral, and Demographical Analysis Type Journal Article
Year 2021 Publication Journal of Medical Internet Research Abbreviated Journal JMIR
Volume 23 Issue (down) 7 Pages e25925
Keywords
Abstract Background: Eating disorders are psychological conditions characterized by unhealthy eating habits. Anorexia nervosa (AN) is defined as the belief of being overweight despite being dangerously underweight. The psychological signs involve emotional and behavioral issues. There is evidence that signs and symptoms can manifest on social media, wherein both harmful and beneficial content is shared daily.
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Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
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Notes ISE Approved no
Call Number Admin @ si @ RFB2021 Serial 3665
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Author Zhen Xu; Sergio Escalera; Adrien Pavao; Magali Richard; Wei-Wei Tu; Quanming Yao; Huan Zhao; Isabelle Guyon
Title Codabench: Flexible, easy-to-use, and reproducible meta-benchmark platform Type Journal Article
Year 2022 Publication Patterns Abbreviated Journal PATTERNS
Volume 3 Issue (down) 7 Pages 100543
Keywords Machine learning; data science; benchmark platform; reproducibility; competitions
Abstract Obtaining a standardized benchmark of computational methods is a major issue in data-science communities. Dedicated frameworks enabling fair benchmarking in a unified environment are yet to be developed. Here, we introduce Codabench, a meta-benchmark platform that is open sourced and community driven for benchmarking algorithms or software agents versus datasets or tasks. A public instance of Codabench is open to everyone free of charge and allows benchmark organizers to fairly compare submissions under the same setting (software, hardware, data, algorithms), with custom protocols and data formats. Codabench has unique features facilitating easy organization of flexible and reproducible benchmarks, such as the possibility of reusing templates of benchmarks and supplying compute resources on demand. Codabench has been used internally and externally on various applications, receiving more than 130 users and 2,500 submissions. As illustrative use cases, we introduce four diverse benchmarks covering graph machine learning, cancer heterogeneity, clinical diagnosis, and reinforcement learning.
Address June 24, 2022
Corporate Author Thesis
Publisher Science Direct Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
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ISSN ISBN Medium
Area Expedition Conference
Notes HuPBA Approved no
Call Number Admin @ si @ XEP2022 Serial 3764
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Author Carlos Martin-Isla; Victor M Campello; Cristian Izquierdo; Kaisar Kushibar; Carla Sendra Balcells; Polyxeni Gkontra; Alireza Sojoudi; Mitchell J Fulton; Tewodros Weldebirhan Arega; Kumaradevan Punithakumar; Lei Li; Xiaowu Sun; Yasmina Al Khalil; Di Liu; Sana Jabbar; Sandro Queiros; Francesco Galati; Moona Mazher; Zheyao Gao; Marcel Beetz; Lennart Tautz; Christoforos Galazis; Marta Varela; Markus Hullebrand; Vicente Grau; Xiahai Zhuang; Domenec Puig; Maria A Zuluaga; Hassan Mohy Ud Din; Dimitris Metaxas; Marcel Breeuwer; Rob J van der Geest; Michelle Noga; Stephanie Bricq; Mark E Rentschler; Andrea Guala; Steffen E Petersen; Sergio Escalera; Jose F Rodriguez Palomares; Karim Lekadir
Title Deep Learning Segmentation of the Right Ventricle in Cardiac MRI: The M&ms Challenge Type Journal Article
Year 2023 Publication IEEE Journal of Biomedical and Health Informatics Abbreviated Journal JBHI
Volume 27 Issue (down) 7 Pages 3302-3313
Keywords
Abstract In recent years, several deep learning models have been proposed to accurately quantify and diagnose cardiac pathologies. These automated tools heavily rely on the accurate segmentation of cardiac structures in MRI images. However, segmentation of the right ventricle is challenging due to its highly complex shape and ill-defined borders. Hence, there is a need for new methods to handle such structure's geometrical and textural complexities, notably in the presence of pathologies such as Dilated Right Ventricle, Tricuspid Regurgitation, Arrhythmogenesis, Tetralogy of Fallot, and Inter-atrial Communication. The last MICCAI challenge on right ventricle segmentation was held in 2012 and included only 48 cases from a single clinical center. As part of the 12th Workshop on Statistical Atlases and Computational Models of the Heart (STACOM 2021), the M&Ms-2 challenge was organized to promote the interest of the research community around right ventricle segmentation in multi-disease, multi-view, and multi-center cardiac MRI. Three hundred sixty CMR cases, including short-axis and long-axis 4-chamber views, were collected from three Spanish hospitals using nine different scanners from three different vendors, and included a diverse set of right and left ventricle pathologies. The solutions provided by the participants show that nnU-Net achieved the best results overall. However, multi-view approaches were able to capture additional information, highlighting the need to integrate multiple cardiac diseases, views, scanners, and acquisition protocols to produce reliable automatic cardiac segmentation algorithms.
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Notes HUPBA Approved no
Call Number Admin @ si @ MCI2023 Serial 3880
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Author Antoni Rosell; Sonia Baeza; S. Garcia-Reina; JL. Mate; Ignasi Guasch; I. Nogueira; I. Garcia-Olive; Guillermo Torres; Carles Sanchez; Debora Gil
Title Radiomics to increase the effectiveness of lung cancer screening programs. Radiolung preliminary results. Type Journal Article
Year 2022 Publication European Respiratory Journal Abbreviated Journal ERJ
Volume 60 Issue (down) 66 Pages
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Abstract
Address
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Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
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Notes IAM Approved no
Call Number Admin @ si @ RBG2022c Serial 3835
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Author A. Pujol; Jordi Vitria; Felipe Lumbreras; Juan J. Villanueva
Title Topological principal component analysis for face encoding and recognition Type Journal Article
Year 2001 Publication Pattern Recognition Letters Abbreviated Journal PRL
Volume 22 Issue (down) 6-7 Pages 769–776
Keywords
Abstract IF: 0.552
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Publisher Place of Publication Editor
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Series Editor Series Title Abbreviated Series Title
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Notes ADAS;OR;MV Approved no
Call Number ADAS @ adas @ PVL2001 Serial 155
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Author Trevor Canham; Javier Vazquez; Elise Mathieu; Marcelo Bertalmío
Title Matching visual induction effects on screens of different size Type Journal Article
Year 2021 Publication Journal of Vision Abbreviated Journal JOV
Volume 21 Issue (down) 6(10) Pages 1-22
Keywords
Abstract In the film industry, the same movie is expected to be watched on displays of vastly different sizes, from cinema screens to mobile phones. But visual induction, the perceptual phenomenon by which the appearance of a scene region is affected by its surroundings, will be different for the same image shown on two displays of different dimensions. This phenomenon presents a practical challenge for the preservation of the artistic intentions of filmmakers, because it can lead to shifts in image appearance between viewing destinations. In this work, we show that a neural field model based on the efficient representation principle is able to predict induction effects and how, by regularizing its associated energy functional, the model is still able to represent induction but is now invertible. From this finding, we propose a method to preprocess an image in a screen–size dependent way so that its perception, in terms of visual induction, may remain constant across displays of different size. The potential of the method is demonstrated through psychophysical experiments on synthetic images and qualitative examples on natural images.
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Notes CIC Approved no
Call Number Admin @ si @ CVM2021 Serial 3595
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Author Javier Vazquez; J. Kevin O'Regan; Maria Vanrell; Graham D. Finlayson
Title A new spectrally sharpened basis to predict colour naming, unique hues, and hue cancellation Type Journal Article
Year 2012 Publication Journal of Vision Abbreviated Journal VSS
Volume 12 Issue (down) 6 (7) Pages 1-14
Keywords
Abstract When light is reflected off a surface, there is a linear relation between the three human photoreceptor responses to the incoming light and the three photoreceptor responses to the reflected light. Different colored surfaces have different linear relations. Recently, Philipona and O'Regan (2006) showed that when this relation is singular in a mathematical sense, then the surface is perceived as having a highly nameable color. Furthermore, white light reflected by that surface is perceived as corresponding precisely to one of the four psychophysically measured unique hues. However, Philipona and O'Regan's approach seems unrelated to classical psychophysical models of color constancy. In this paper we make this link. We begin by transforming cone sensors to spectrally sharpened counterparts. In sharp color space, illumination change can be modeled by simple von Kries type scalings of response values within each of the spectrally sharpened response channels. In this space, Philipona and O'Regan's linear relation is captured by a simple Land-type color designator defined by dividing reflected light by incident light. This link between Philipona and O'Regan's theory and Land's notion of color designator gives the model biological plausibility. We then show that Philipona and O'Regan's singular surfaces are surfaces which are very close to activating only one or only two of such newly defined spectrally sharpened sensors, instead of the usual three. Closeness to zero is quantified in a new simplified measure of singularity which is also shown to relate to the chromaticness of colors. As in Philipona and O'Regan's original work, our new theory accounts for a large variety of psychophysical color data.
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Notes CIC Approved no
Call Number Admin @ si @ VOV2012 Serial 1998
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Author F. Moreso; D. Seron; Jordi Vitria; J.M. Grinyo; F.M. Colome-Serra; N. Pares; J.R. Serra
Title Quantification of Interstitial Chronic Renal Damage by means of Texture Analysis. Type Journal Article
Year 1994 Publication Kidney International Abbreviated Journal
Volume 46 Issue (down) 6 Pages 1721-1727
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Abstract
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Notes OR;MV Approved no
Call Number BCNPCL @ bcnpcl @ MSV1994 Serial 113
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Author Oriol Ramos Terrades; Ernest Valveny
Title A new use of the ridgelets transform for describing linear singularities in images Type Journal Article
Year 2006 Publication Pattern Recognition Letters Abbreviated Journal PRL
Volume 27 Issue (down) 6 Pages 587–596
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Abstract
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes DAG Approved no
Call Number DAG @ dag @ RaV2006a Serial 635
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Author Francisco Javier Orozco; Xavier Roca; Jordi Gonzalez
Title Real-Time Gaze Tracking with Appearance-Based Models Type Journal Article
Year 2008 Publication Machine Vision Applications Abbreviated Journal MVAP
Volume 20 Issue (down) 6 Pages 353-364
Keywords Keywords Eyelid and iris tracking, Appearance models, Blinking, Iris saccade, Real-time gaze tracking
Abstract Psychological evidence has emphasized the importance of eye gaze analysis in human computer interaction and emotion interpretation. To this end, current image analysis algorithms take into consideration eye-lid and iris motion detection using colour information and edge detectors. However, eye movement is fast and and hence difficult to use to obtain a precise and robust tracking. Instead, our
method proposed to describe eyelid and iris movements as continuous variables using appearance-based tracking. This approach combines the strengths of adaptive appearance models, optimization methods and backtracking techniques.Thus,
in the proposed method textures are learned on-line from near frontal images and illumination changes, occlusions and fast movements are managed. The method achieves real-time performance by combining two appearance-based trackers to a
backtracking algorithm for eyelid estimation and another for iris estimation. These contributions represent a significant advance towards a reliable gaze motion description for HCI and expression analysis, where the strength of complementary
methodologies are combined to avoid using high quality images, colour information, texture training, camera settings and other time-consuming processes.
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Series Editor Series Title Abbreviated Series Title
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Notes ISE Approved no
Call Number ISE @ ise @ ORG2008 Serial 972
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Author Jaume Garcia; Debora Gil; Sandra Pujades; Francesc Carreras
Title A Variational Framework for Assessment of the Left Ventricle Motion Type Journal Article
Year 2008 Publication International Journal Mathematical Modelling of Natural Phenomena Abbreviated Journal
Volume 3 Issue (down) 6 Pages 76-100
Keywords Key words: Left Ventricle Dynamics, Ventricular Torsion, Tagged Magnetic Resonance, Motion Tracking, Variational Framework, Gabor Transform.
Abstract Impairment of left ventricular contractility due to cardiovascular diseases is reflected in left ventricle (LV) motion patterns. An abnormal change of torsion or long axis shortening LV values can help with the diagnosis and follow-up of LV dysfunction. Tagged Magnetic Resonance (TMR) is a widely spread medical imaging modality that allows estimation of the myocardial tissue local deformation. In this work, we introduce a novel variational framework for extracting the left ventricle dynamics from TMR sequences. A bi-dimensional representation space of TMR images given by Gabor filter banks is defined. Tracking of the phases of the Gabor response is combined using a variational framework which regularizes the deformation field just at areas where the Gabor amplitude drops, while restoring the underlying motion otherwise. The clinical applicability of the proposed method is illustrated by extracting normality models of the ventricular torsion from 19 healthy subjects.
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Notes IAM Approved no
Call Number IAM @ iam @ GGC2008a Serial 1058
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Author Santiago Segui; Michal Drozdzal; Ekaterina Zaytseva; Fernando Azpiroz; Petia Radeva; Jordi Vitria
Title Detection of wrinkle frames in endoluminal videos using betweenness centrality measures for images Type Journal Article
Year 2014 Publication IEEE Transactions on Information Technology in Biomedicine Abbreviated Journal TITB
Volume 18 Issue (down) 6 Pages 1831-1838
Keywords Wireless Capsule Endoscopy; Small Bowel Motility Dysfunction; Contraction Detection; Structured Prediction; Betweenness Centrality
Abstract Intestinal contractions are one of the most important events to diagnose motility pathologies of the small intestine. When visualized by wireless capsule endoscopy (WCE), the sequence of frames that represents a contraction is characterized by a clear wrinkle structure in the central frames that corresponds to the folding of the intestinal wall. In this paper we present a new method to robustly detect wrinkle frames in full WCE videos by using a new mid-level image descriptor that is based on a centrality measure proposed for graphs. We present an extended validation, carried out in a very large database, that shows that the proposed method achieves state of the art performance for this task.
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Series Editor Series Title Abbreviated Series Title
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Notes OR; MILAB; 600.046;MV Approved no
Call Number Admin @ si @ SDZ2014 Serial 2385
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Author Carlo Gatta; Oriol Pujol; Oriol Rodriguez-Leor; J. M. Ferre; Petia Radeva
Title Fast Rigid Registration of Vascular Structures in IVUS Sequences Type Journal Article
Year 2009 Publication IEEE Transactions on Information Technology in Biomedicine Abbreviated Journal
Volume 13 Issue (down) 6 Pages 106-1011
Keywords
Abstract Intravascular ultrasound (IVUS) technology permits visualization of high-resolution images of internal vascular structures. IVUS is a unique image-guiding tool to display longitudinal view of the vessels, and estimate the length and size of vascular structures with the goal of accurate diagnosis. Unfortunately, due to pulsatile contraction and expansion of the heart, the captured images are affected by different motion artifacts that make visual inspection difficult. In this paper, we propose an efficient algorithm that aligns vascular structures and strongly reduces the saw-shaped oscillation, simplifying the inspection of longitudinal cuts; it reduces the motion artifacts caused by the displacement of the catheter in the short-axis plane and the catheter rotation due to vessel tortuosity. The algorithm prototype aligns 3.16 frames/s and clearly outperforms state-of-the-art methods with similar computational cost. The speed of the algorithm is crucial since it allows to inspect the corrected sequence during patient intervention. Moreover, we improved an indirect methodology for IVUS rigid registration algorithm evaluation.
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Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1089-7771 ISBN Medium
Area Expedition Conference
Notes MILAB;HuPBA Approved no
Call Number BCNPCL @ bcnpcl @ GPL2009 Serial 1250
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