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	Author	Craig Von Land; Ricardo Toledo; Juan J. Villanueva
	Title	TeleRegions: Application of Telematics in Cardiac Care.			Type	Miscellaneous
	Year	1997	Publication	Computers in Cardiology, 1997. Piscataway, NJ: IEEE Computer Society Press, 24: 645–8.	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	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	ADAS			Approved	no
	Call Number	ISE @ ise @ VTV1997			Serial	64
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	Author	Craig Von Land; Ricardo Toledo; Juan J. Villanueva
	Title	CARE: Computer Assisted Radiology Environment			Type	Miscellaneous
	Year	1996	Publication	Tecnologia de Imagenes Medicas, Convencion Iberoamericana sobre la Salud en la Sociedad Global de la Informacion.	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract
	Address	Buenos Aires, Argentina
	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	ADAS			Approved	no
	Call Number	ISE @ ise @ VTV1996a			Serial	99
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	Author	Craig Von Land; Ricardo Toledo; Juan J. Villanueva
	Title	TeleRegion: Tele-Applications for European Regions			Type	Miscellaneous
	Year	1996	Publication	Experiencias de validacion, uso y expansion de la telematica a nivel regional e inter–regional. Convencion Iberoamericana sobre la Salud en la Sociedad Global de la Informacion.	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract
	Address	Buenos Aires, Argentina
	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	ADAS			Approved	no
	Call Number	ISE @ ise @ VTV1996b			Serial	101
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	Author	Craig Von Land; Ricardo Toledo; Juan J. Villanueva
	Title	Object Oriented Design of the DICOM standard			Type	Miscellaneous
	Year	1996	Publication	International Symposium on Cardiovascular Imaging.	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract
	Address	Leiden, The Netherlands
	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	ADAS			Approved	no
	Call Number	ISE @ ise @ VTV1996c			Serial	104
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	Author	Corina Krauter; Ursula Reiter; Albrecht Schmidt; Marc Masana; Rudolf Stollberger; Michael Fuchsjager; Gert Reiter
	Title	Objective extraction of the temporal evolution of the mitral valve vortex ring from 4D flow MRI			Type	Conference Article
	Year	2019	Publication	27th Annual Meeting & Exhibition of the International Society for Magnetic Resonance in Medicine	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	The mitral valve vortex ring is a promising flow structure for analysis of diastolic function, however, methods for objective extraction of its formation to dissolution are lacking. We present a novel algorithm for objective extraction of the temporal evolution of the mitral valve vortex ring from magnetic resonance 4D flow data and validated the method against visual analysis. The algorithm successfully extracted mitral valve vortex rings during both early- and late-diastolic filling and agreed substantially with visual assessment. Early-diastolic mitral valve vortex ring properties differed between healthy subjects and patients with ischemic heart disease.
	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	ISMRM
	Notes	LAMP; 600.120			Approved	no
	Call Number	Admin @ si @ KRS2019			Serial	3300
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	Author	Clementine Decamps; Alexis Arnaud; Florent Petitprez; Mira Ayadi; Aurelia Baures; Lucile Armenoult; Sergio Escalera; Isabelle Guyon; Remy Nicolle; Richard Tomasini; Aurelien de Reynies; Jerome Cros; Yuna Blum; Magali Richard
	Title	DECONbench: a benchmarking platform dedicated to deconvolution methods for tumor heterogeneity quantification			Type	Journal Article
	Year	2021	Publication	BMC Bioinformatics	Abbreviated Journal
	Volume	22	Issue		Pages	473
	Keywords
	Abstract	Quantification of tumor heterogeneity is essential to better understand cancer progression and to adapt therapeutic treatments to patient specificities. Bioinformatic tools to assess the different cell populations from single-omic datasets as bulk transcriptome or methylome samples have been recently developed, including reference-based and reference-free methods. Improved methods using multi-omic datasets are yet to be developed in the future and the community would need systematic tools to perform a comparative evaluation of these algorithms on controlled data.
	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	HUPBA; no proj			Approved	no
	Call Number	Admin @ si @ DAP2021			Serial	3650
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	Author	Clement Guerin; Christophe Rigaud; Karell Bertet; Jean-Christophe Burie; Arnaud Revel ; Jean-Marc Ogier
	Title	Réduction de l’espace de recherche pour les personnages de bandes dessinées			Type	Conference Article
	Year	2014	Publication	19th National Congress Reconnaissance de Formes et l'Intelligence Artificielle	Abbreviated Journal
	Volume		Issue		Pages
	Keywords	contextual search; document analysis; comics characters
	Abstract	Les bandes dessinées représentent un patrimoine culturel important dans de nombreux pays et leur numérisation massive offre la possibilité d'effectuer des recherches dans le contenu des images. À ce jour, ce sont principalement les structures des pages et leurs contenus textuels qui ont été étudiés, peu de travaux portent sur le contenu graphique. Nous proposons de nous appuyer sur des éléments déjà étudiés tels que la position des cases et des bulles, pour réduire l'espace de recherche et localiser les personnages en fonction de la queue des bulles. L'évaluation de nos différentes contributions à partir de la base eBDtheque montre un taux de détection des queues de bulle de 81.2%, de localisation des personnages allant jusqu'à 85% et un gain d'espace de recherche de plus de 50%.
	Address	Rouen; Francia; July 2014
	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	RFIA
	Notes	DAG; 600.077			Approved	no
	Call Number	Admin @ si @ GRB2014			Serial	2480
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	Author	Claudio Baecchi; Francesco Turchini; Lorenzo Seidenari; Andrew Bagdanov; Alberto del Bimbo
	Title	Fisher vectors over random density forest for object recognition			Type	Conference Article
	Year	2014	Publication	22nd International Conference on Pattern Recognition	Abbreviated Journal
	Volume		Issue		Pages	4328-4333
	Keywords
	Abstract
	Address	Stockholm; Sweden; August 2014
	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	ICPR
	Notes	LAMP; 600.079			Approved	no
	Call Number	Admin @ si @ BTS2014			Serial	2518
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	Author	Claudia Greco; Carmela Buono; Pau Buch-Cardona; Gennaro Cordasco; Sergio Escalera; Anna Esposito; Anais Fernandez; Daria Kyslitska; Maria Stylianou Kornes; Cristina Palmero; Jofre Tenorio Laranga; Anna Torp Johansen; Maria Ines Torres
	Title	Emotional Features of Interactions With Empathic Agents			Type	Conference Article
	Year	2021	Publication	IEEE/CVF International Conference on Computer Vision Workshops	Abbreviated Journal
	Volume		Issue		Pages	2168-2176
	Keywords
	Abstract	The current study is part of the EMPATHIC project, whose aim is to develop an Empathic Virtual Coach (VC) capable of promoting healthy and independent aging. To this end, the VC needs to be capable of perceiving the emotional states of users and adjusting its behaviour during the interactions according to what the users are experiencing in terms of emotions and comfort. Thus, the present work focuses on some sessions where elderly users of three different countries interact with a simulated system. Audio and video information extracted from these sessions were examined by external observers to assess participants' emotional experience with the EMPATHIC-VC in terms of categorical and dimensional assessment of emotions. Analyses were conducted on the emotional labels assigned by the external observers while participants were engaged in two different scenarios: a generic one, where the interaction was carried out with no intention to discuss a specific topic, and a nutrition one, aimed to accomplish a conversation on users' nutritional habits. Results of analyses performed on both audio and video data revealed that the EMPATHIC coach did not elicit negative feelings in the users. Indeed, users from all countries have shown relaxed and positive behavior when interacting with the simulated VC during both scenarios. Overall, the EMPATHIC-VC was capable to offer an enjoyable experience without eliciting negative feelings in the users. This supports the hypothesis that an Empathic Virtual Coach capable of considering users' expectations and emotional states could support elderly people in daily life activities and help them to remain independent.
	Address	VIRTUAL; October 2021
	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	ICCVW
	Notes	HUPBA; no proj			Approved	no
	Call Number	Admin @ si @ GBB2021			Serial	3647
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	Author	Ciprian Corneanu; Sergio Escalera; Aleix M. Martinez
	Title	Computing the Testing Error Without a Testing Set			Type	Conference Article
	Year	2020	Publication	33rd IEEE Conference on Computer Vision and Pattern Recognition	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	Oral. Paper award nominee. Deep Neural Networks (DNNs) have revolutionized computer vision. We now have DNNs that achieve top (performance) results in many problems, including object recognition, facial expression analysis, and semantic segmentation, to name but a few. The design of the DNNs that achieve top results is, however, non-trivial and mostly done by trailand-error. That is, typically, researchers will derive many DNN architectures (i.e., topologies) and then test them on multiple datasets. However, there are no guarantees that the selected DNN will perform well in the real world. One can use a testing set to estimate the performance gap between the training and testing sets, but avoiding overfitting-to-thetesting-data is almost impossible. Using a sequestered testing dataset may address this problem, but this requires a constant update of the dataset, a very expensive venture. Here, we derive an algorithm to estimate the performance gap between training and testing that does not require any testing dataset. Specifically, we derive a number of persistent topology measures that identify when a DNN is learning to generalize to unseen samples. This allows us to compute the DNN’s testing error on unseen samples, even when we do not have access to them. We provide extensive experimental validation on multiple networks and datasets to demonstrate the feasibility of the proposed approach.
	Address	Virtual CVPR
	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	CVPR
	Notes	HuPBA; no proj			Approved	no
	Call Number	Admin @ si @ CEM2020			Serial	3437
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	Author	Ciprian Corneanu; Meysam Madadi; Sergio Escalera; Aleix Martinez
	Title	Explainable Early Stopping for Action Unit Recognition			Type	Conference Article
	Year	2020	Publication	Faces and Gestures in E-health and welfare workshop	Abbreviated Journal
	Volume		Issue		Pages	693-699
	Keywords
	Abstract	A common technique to avoid overfitting when training deep neural networks (DNN) is to monitor the performance in a dedicated validation data partition and to stop training as soon as it saturates. This only focuses on what the model does, while completely ignoring what happens inside it. In this work, we open the “black-box” of DNN in order to perform early stopping. We propose to use a novel theoretical framework that analyses meso-scale patterns in the topology of the functional graph of a network while it trains. Based on it, we decide when it transitions from learning towards overfitting in a more explainable way. We exemplify the benefits of this approach on a state-of-the art custom DNN that jointly learns local representations and label structure employing an ensemble of dedicated subnetworks. We show that it is practically equivalent in performance to early stopping with patience, the standard early stopping algorithm in the literature. This proves beneficial for AU recognition performance and provides new insights into how learning of AUs occurs in DNNs.
	Address	Virtual; November 2020
	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	FGW
	Notes	HUPBA;			Approved	no
	Call Number	Admin @ si @ CME2020			Serial	3514
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	Author	Ciprian Corneanu; Meysam Madadi; Sergio Escalera; Aleix M. Martinez
	Title	What does it mean to learn in deep networks? And, how does one detect adversarial attacks?			Type	Conference Article
	Year	2019	Publication	32nd IEEE Conference on Computer Vision and Pattern Recognition	Abbreviated Journal
	Volume		Issue		Pages	4752-4761
	Keywords
	Abstract	The flexibility and high-accuracy of Deep Neural Networks (DNNs) has transformed computer vision. But, the fact that we do not know when a specific DNN will work and when it will fail has resulted in a lack of trust. A clear example is self-driving cars; people are uncomfortable sitting in a car driven by algorithms that may fail under some unknown, unpredictable conditions. Interpretability and explainability approaches attempt to address this by uncovering what a DNN models, i.e., what each node (cell) in the network represents and what images are most likely to activate it. This can be used to generate, for example, adversarial attacks. But these approaches do not generally allow us to determine where a DNN will succeed or fail and why. i.e., does this learned representation generalize to unseen samples? Here, we derive a novel approach to define what it means to learn in deep networks, and how to use this knowledge to detect adversarial attacks. We show how this defines the ability of a network to generalize to unseen testing samples and, most importantly, why this is the case.
	Address	California; June 2019
	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	CVPR
	Notes	HuPBA; no proj			Approved	no
	Call Number	Admin @ si @ CME2019			Serial	3332
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	Author	Ciprian Corneanu; Meysam Madadi; Sergio Escalera
	Title	Deep Structure Inference Network for Facial Action Unit Recognition			Type	Conference Article
	Year	2018	Publication	15th European Conference on Computer Vision	Abbreviated Journal
	Volume	11216	Issue		Pages	309-324
	Keywords	Computer Vision; Machine Learning; Deep Learning; Facial Expression Analysis; Facial Action Units; Structure Inference
	Abstract	Facial expressions are combinations of basic components called Action Units (AU). Recognizing AUs is key for general facial expression analysis. Recently, efforts in automatic AU recognition have been dedicated to learning combinations of local features and to exploiting correlations between AUs. We propose a deep neural architecture that tackles both problems by combining learned local and global features in its initial stages and replicating a message passing algorithm between classes similar to a graphical model inference approach in later stages. We show that by training the model end-to-end with increased supervision we improve state-of-the-art by 5.3% and 8.2% performance on BP4D and DISFA datasets, respectively.
	Address	Munich; September 2018
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title	LNCS
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	ECCV
	Notes	HUPBA; no proj			Approved	no
	Call Number	Admin @ si @ CME2018			Serial	3205
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	Author	Ciprian Corneanu; Marc Oliu; Jeffrey F. Cohn; Sergio Escalera
	Title	Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History			Type	Journal Article
	Year	2016	Publication	IEEE Transactions on Pattern Analysis and Machine Intelligence	Abbreviated Journal	TPAMI
	Volume	28	Issue	8	Pages	1548-1568
	Keywords	Facial expression; affect; emotion recognition; RGB; 3D; thermal; multimodal
	Abstract	Facial expressions are an important way through which humans interact socially. Building a system capable of automatically recognizing facial expressions from images and video has been an intense field of study in recent years. Interpreting such expressions remains challenging and much research is needed about the way they relate to human affect. This paper presents a general overview of automatic RGB, 3D, thermal and multimodal facial expression analysis. We define a new taxonomy for the field, encompassing all steps from face detection to facial expression recognition, and describe and classify the state of the art methods accordingly. We also present the important datasets and the bench-marking of most influential methods. We conclude with a general discussion about trends, important questions and future lines of research.
	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	HuPBA;MILAB;			Approved	no
	Call Number	Admin @ si @ COC2016			Serial	2718
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	Author	ChunYang; Xu Cheng Yin; Hong Yu; Dimosthenis Karatzas; Yu Cao
	Title	ICDAR2017 Robust Reading Challenge on Text Extraction from Biomedical Literature Figures (DeTEXT)			Type	Conference Article
	Year	2017	Publication	14th International Conference on Document Analysis and Recognition	Abbreviated Journal
	Volume		Issue		Pages	1444-1447
	Keywords
	Abstract	Hundreds of millions of figures are available in the biomedical literature, representing important biomedical experimental evidence. Since text is a rich source of information in figures, automatically extracting such text may assist in the task of mining figure information and understanding biomedical documents. Unlike images in the open domain, biomedical figures present a variety of unique challenges. For example, biomedical figures typically have complex layouts, small font sizes, short text, specific text, complex symbols and irregular text arrangements. This paper presents the final results of the ICDAR 2017 Competition on Text Extraction from Biomedical Literature Figures (ICDAR2017 DeTEXT Competition), which aims at extracting (detecting and recognizing) text from biomedical literature figures. Similar to text extraction from scene images and web pictures, ICDAR2017 DeTEXT Competition includes three major tasks, i.e., text detection, cropped word recognition and end-to-end text recognition. Here, we describe in detail the data set, tasks, evaluation protocols and participants of this competition, and report the performance of the participating methods.
	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	978-1-5386-3586-5	Medium
	Area		Expedition		Conference	ICDAR
	Notes	DAG; 600.121			Approved	no
	Call Number	Admin @ si @ YCY2017			Serial	3098
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