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	Author	Joan M. Nuñez
	Title	Vascular Pattern Characterization in Colonoscopy Images			Type	Book Whole
	Year	2015	Publication	PhD Thesis, Universitat Autonoma de Barcelona-CVC	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	Colorectal cancer is the third most common cancer worldwide and the second most common malignant tumor in Europe. Screening tests have shown to be very eective in increasing the survival rates since they allow an early detection of polyps. Among the dierent screening techniques, colonoscopy is considered the gold standard although clinical studies mention several problems that have an impact in the quality of the procedure. The navigation through the rectum and colon track can be challenging for the physicians which can increase polyp miss rates. The thorough visualization of the colon track must be ensured so that the chances of missing lesions are minimized. The visual analysis of colonoscopy images can provide important information to the physicians and support their navigation during the procedure. Blood vessels and their branching patterns can provide descriptive power to potentially develop biometric markers. Anatomical markers based on blood vessel patterns could be used to identify a particular scene in colonoscopy videos and to support endoscope navigation by generating a sequence of ordered scenes through the dierent colon sections. By verifying the presence of vascular content in the endoluminal scene it is also possible to certify a proper inspection of the colon mucosa and to improve polyp localization. Considering the potential uses of blood vessel description, this contribution studies the characterization of the vascular content and the analysis of the descriptive power of its branching patterns. Blood vessel characterization in colonoscopy images is shown to be a challenging task. The endoluminal scene is conformed by several elements whose similar characteristics hinder the development of particular models for each of them. To overcome such diculties we propose the use of the blood vessel branching characteristics as key features for pattern description. We present a model to characterize junctions in binary patterns. The implementation of the junction model allows us to develop a junction localization method. We created two data sets including manually labeled vessel information as well as manual ground truths of two types of keypoint landmarks: junctions and endpoints. The proposed method outperforms the available algorithms in the literature in experiments in both, our newly created colon vessel data set, and in DRIVE retinal fundus image data set. In the latter case, we created a manual ground truth of junction coordinates. Since we want to explore the descriptive potential of junctions and vessels, we propose a graph-based approach to create anatomical markers. In the context of polyp localization, we present a new method to inhibit the in uence of blood vessels in the extraction valley-prole information. The results show that our methodology decreases vessel in uence, increases polyp information and leads to an improvement in state-of-the-art polyp localization performance. We also propose a polyp-specic segmentation method that outperforms other general and specic approaches.
	Address	November 2015
	Corporate Author				Thesis	Ph.D. thesis
	Publisher	Ediciones Graficas Rey	Place of Publication		Editor	Fernando Vilariño
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN	978-84-943427-6-9	Medium
	Area		Expedition		Conference
	Notes	MV			Approved	no
	Call Number	Admin @ si @ Nuñ2015			Serial	2709
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	Author	Michal Drozdzal; Santiago Segui; Petia Radeva; Carolina Malagelada; Fernando Azpiroz; Jordi Vitria
	Title	Motility bar: a new tool for motility analysis of endoluminal videos			Type	Journal Article
	Year	2015	Publication	Computers in Biology and Medicine	Abbreviated Journal	CBM
	Volume	65	Issue		Pages	320-330
	Keywords	Small intestine; Motility; WCE; Computer vision; Image classification
	Abstract	Wireless Capsule Endoscopy (WCE) provides a new perspective of the small intestine, since it enables, for the first time, visualization of the entire organ. However, the long visual video analysis time, due to the large number of data in a single WCE study, was an important factor impeding the widespread use of the capsule as a tool for intestinal abnormalities detection. Therefore, the introduction of WCE triggered a new field for the application of computational methods, and in particular, of computer vision. In this paper, we follow the computational approach and come up with a new perspective on the small intestine motility problem. Our approach consists of three steps: first, we review a tool for the visualization of the motility information contained in WCE video; second, we propose algorithms for the characterization of two motility building-blocks: contraction detector and lumen size estimation; finally, we introduce an approach to detect segments of stable motility behavior. Our claims are supported by an evaluation performed with 10 WCE videos, suggesting that our methods ably capture the intestinal motility information.
	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	MILAB;MV			Approved	no
	Call Number	Admin @ si @ DSR2015			Serial	2635
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	Author	Carolina Malagelada; Michal Drozdzal; Santiago Segui; Sara Mendez; Jordi Vitria; Petia Radeva; Javier Santos; Anna Accarino; Juan R. Malagelada; Fernando Azpiroz
	Title	Classification of functional bowel disorders by objective physiological criteria based on endoluminal image analysis			Type	Journal Article
	Year	2015	Publication	American Journal of Physiology-Gastrointestinal and Liver Physiology	Abbreviated Journal	AJPGI
	Volume	309	Issue	6	Pages	G413--G419
	Keywords	capsule endoscopy; computer vision analysis; functional bowel disorders; intestinal motility; machine learning
	Abstract	We have previously developed an original method to evaluate small bowel motor function based on computer vision analysis of endoluminal images obtained by capsule endoscopy. Our aim was to demonstrate intestinal motor abnormalities in patients with functional bowel disorders by endoluminal vision analysis. Patients with functional bowel disorders (n = 205) and healthy subjects (n = 136) ingested the endoscopic capsule (Pillcam-SB2, Given-Imaging) after overnight fast and 45 min after gastric exit of the capsule a liquid meal (300 ml, 1 kcal/ml) was administered. Endoluminal image analysis was performed by computer vision and machine learning techniques to define the normal range and to identify clusters of abnormal function. After training the algorithm, we used 196 patients and 48 healthy subjects, completely naive, as test set. In the test set, 51 patients (26%) were detected outside the normal range (P < 0.001 vs. 3 healthy subjects) and clustered into hypo- and hyperdynamic subgroups compared with healthy subjects. Patients with hypodynamic behavior (n = 38) exhibited less luminal closure sequences (41 ± 2% of the recording time vs. 61 ± 2%; P < 0.001) and more static sequences (38 ± 3 vs. 20 ± 2%; P < 0.001); in contrast, patients with hyperdynamic behavior (n = 13) had an increased proportion of luminal closure sequences (73 ± 4 vs. 61 ± 2%; P = 0.029) and more high-motion sequences (3 ± 1 vs. 0.5 ± 0.1%; P < 0.001). Applying an original methodology, we have developed a novel classification of functional gut disorders based on objective, physiological criteria of small bowel function.
	Address
	Corporate Author				Thesis
	Publisher	American Physiological Society	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	MILAB; OR;MV			Approved	no
	Call Number	Admin @ si @ MDS2015			Serial	2666
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	Author	Santiago Segui; Oriol Pujol; Jordi Vitria
	Title	Learning to count with deep object features			Type	Conference Article
	Year	2015	Publication	Deep Vision: Deep Learning in Computer Vision, CVPR 2015 Workshop	Abbreviated Journal
	Volume		Issue		Pages	90-96
	Keywords
	Abstract	Learning to count is a learning strategy that has been recently proposed in the literature for dealing with problems where estimating the number of object instances in a scene is the final objective. In this framework, the task of learning to detect and localize individual object instances is seen as a harder task that can be evaded by casting the problem as that of computing a regression value from hand-crafted image features. In this paper we explore the features that are learned when training a counting convolutional neural network in order to understand their underlying representation. To this end we define a counting problem for MNIST data and show that the internal representation of the network is able to classify digits in spite of the fact that no direct supervision was provided for them during training. We also present preliminary results about a deep network that is able to count the number of pedestrians in a scene.
	Address	Boston; USA; June 2015
	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	CVPRW
	Notes	MILAB; HuPBA; OR;MV			Approved	no
	Call Number	Admin @ si @ SPV2015			Serial	2636
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	Author	Adriana Romero; Petia Radeva; Carlo Gatta
	Title	Meta-parameter free unsupervised sparse feature learning			Type	Journal Article
	Year	2015	Publication	IEEE Transactions on Pattern Analysis and Machine Intelligence	Abbreviated Journal	TPAMI
	Volume	37	Issue	8	Pages	1716-1722
	Keywords
	Abstract	We propose a meta-parameter free, off-the-shelf, simple and fast unsupervised feature learning algorithm, which exploits a new way of optimizing for sparsity. Experiments on CIFAR-10, STL- 10 and UCMerced show that the method achieves the state-of-theart performance, providing discriminative features that generalize well.
	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	MILAB; 600.068; 600.079; 601.160			Approved	no
	Call Number	Admin @ si @ RRG2014b			Serial	2594
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	Author	G. Zahnd; Simone Balocco; A. Serusclat; P. Moulin; M. Orkisz; D. Vray
	Title	Progressive attenuation of the longitudinal kinetics in the common carotid artery: preliminary in vivo assessment Ultrasound in Medicine and Biology			Type	Journal Article
	Year	2015	Publication	Ultrasound in Medicine and Biology	Abbreviated Journal	UMB
	Volume	41	Issue	1	Pages	339-345
	Keywords	Arterial stiffness; Atherosclerosis; Common carotid artery; Longitudinal kinetics; Motion tracking; Ultrasound imaging
	Abstract	Longitudinal kinetics (LOKI) of the arterial wall consists of the shearing motion of the intima-media complex over the adventitia layer in the direction parallel to the blood flow during the cardiac cycle. The aim of this study was to investigate the local variability of LOKI amplitude along the length of the vessel. By use of a previously validated motion-estimation framework, 35 in vivo longitudinal B-mode ultrasound cine loops of healthy common carotid arteries were analyzed. Results indicated that LOKI amplitude is progressively attenuated along the length of the artery, as it is larger in regions located on the proximal side of the image (i.e., toward the heart) and smaller in regions located on the distal side of the image (i.e., toward the head), with an average attenuation coefficient of -2.5 ± 2.0%/mm. Reported for the first time in this study, this phenomenon is likely to be of great importance in improving understanding of atherosclerosis mechanisms, and has the potential to be a novel index of arterial stiffness.
	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	MILAB			Approved	no
	Call Number	Admin @ si @ ZBS2014			Serial	2556
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	Author	Adriana Romero; Nicolas Ballas; Samira Ebrahimi Kahou; Antoine Chassang; Carlo Gatta; Yoshua Bengio
	Title	FitNets: Hints for Thin Deep Nets			Type	Conference Article
	Year	2015	Publication	3rd International Conference on Learning Representations ICLR2015	Abbreviated Journal
	Volume		Issue		Pages
	Keywords	Computer Science ; Learning; Computer Science ;Neural and Evolutionary Computing
	Abstract	While depth tends to improve network performances, it also makes gradient-based training more difficult since deeper networks tend to be more non-linear. The recently proposed knowledge distillation approach is aimed at obtaining small and fast-to-execute models, and it has shown that a student network could imitate the soft output of a larger teacher network or ensemble of networks. In this paper, we extend this idea to allow the training of a student that is deeper and thinner than the teacher, using not only the outputs but also the intermediate representations learned by the teacher as hints to improve the training process and final performance of the student. Because the student intermediate hidden layer will generally be smaller than the teacher's intermediate hidden layer, additional parameters are introduced to map the student hidden layer to the prediction of the teacher hidden layer. This allows one to train deeper students that can generalize better or run faster, a trade-off that is controlled by the chosen student capacity. For example, on CIFAR-10, a deep student network with almost 10.4 times less parameters outperforms a larger, state-of-the-art teacher network.
	Address	San Diego; CA; May 2015
	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	ICLR
	Notes	MILAB			Approved	no
	Call Number	Admin @ si @ RBK2015			Serial	2593
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	Author	Marc Bolaños; Maite Garolera; Petia Radeva
	Title	Object Discovery using CNN Features in Egocentric Videos			Type	Conference Article
	Year	2015	Publication	Pattern Recognition and Image Analysis, Proceedings of 7th Iberian Conference , ibPRIA 2015	Abbreviated Journal
	Volume	9117	Issue		Pages	67-74
	Keywords	Object discovery; Egocentric videos; Lifelogging; CNN
	Abstract	Lifelogging devices based on photo/video are spreading faster everyday. This growth can represent great benefits to develop methods for extraction of meaningful information about the user wearing the device and his/her environment. In this paper, we propose a semi-supervised strategy for easily discovering objects relevant to the person wearing a first-person camera. The egocentric video sequence acquired by the camera, uses both the appearance extracted by means of a deep convolutional neural network and an object refill methodology that allow to discover objects even in case of small amount of object appearance in the collection of images. We validate our method on a sequence of 1000 egocentric daily images and obtain results with an F-measure of 0.5, 0.17 better than the state of the art approach.
	Address	Santiago de Compostela; España; June 2015
	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	0302-9743	ISBN	978-3-319-19389-2	Medium
	Area		Expedition		Conference	IbPRIA
	Notes	MILAB			Approved	no
	Call Number	Admin @ si @ BGR2015			Serial	2596
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	Author	Estefania Talavera; Mariella Dimiccoli; Marc Bolaños; Maedeh Aghaei; Petia Radeva
	Title	R-clustering for egocentric video segmentation			Type	Conference Article
	Year	2015	Publication	Pattern Recognition and Image Analysis, Proceedings of 7th Iberian Conference , ibPRIA 2015	Abbreviated Journal
	Volume	9117	Issue		Pages	327-336
	Keywords	Temporal video segmentation; Egocentric videos; Clustering
	Abstract	In this paper, we present a new method for egocentric video temporal segmentation based on integrating a statistical mean change detector and agglomerative clustering(AC) within an energy-minimization framework. Given the tendency of most AC methods to oversegment video sequences when clustering their frames, we combine the clustering with a concept drift detection technique (ADWIN) that has rigorous guarantee of performances. ADWIN serves as a statistical upper bound for the clustering-based video segmentation. We integrate both techniques in an energy-minimization framework that serves to disambiguate the decision of both techniques and to complete the segmentation taking into account the temporal continuity of video frames descriptors. We present experiments over egocentric sets of more than 13.000 images acquired with different wearable cameras, showing that our method outperforms state-of-the-art clustering methods.
	Address	Santiago de Compostela; España; June 2015
	Corporate Author				Thesis
	Publisher	Springer International Publishing	Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title	LNCS
	Series Volume		Series Issue		Edition
	ISSN	0302-9743	ISBN	978-3-319-19389-2	Medium
	Area		Expedition		Conference	IbPRIA
	Notes	MILAB			Approved	no
	Call Number	Admin @ si @ TDB2015			Serial	2597
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	Author	Marc Bolaños; R. Mestre; Estefania Talavera; Xavier Giro; Petia Radeva
	Title	Visual Summary of Egocentric Photostreams by Representative Keyframes			Type	Conference Article
	Year	2015	Publication	IEEE International Conference on Multimedia and Expo ICMEW2015	Abbreviated Journal
	Volume		Issue		Pages	1-6
	Keywords	egocentric; lifelogging; summarization; keyframes
	Abstract	Building a visual summary from an egocentric photostream captured by a lifelogging wearable camera is of high interest for different applications (e.g. memory reinforcement). In this paper, we propose a new summarization method based on keyframes selection that uses visual features extracted bymeans of a convolutional neural network. Our method applies an unsupervised clustering for dividing the photostreams into events, and finally extracts the most relevant keyframe for each event. We assess the results by applying a blind-taste test on a group of 20 people who assessed the quality of the summaries.
	Address	Torino; italy; July 2015
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue	978-1-4799-7079-7	Edition
	ISSN		ISBN	978-1-4799-7079-7	Medium
	Area		Expedition		Conference	ICME
	Notes	MILAB			Approved	no
	Call Number	Admin @ si @ BMT2015			Serial	2638
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	Author	Tadashi Araki; Nobutaka Ikeda; Nilanjan Dey; Sayan Chakraborty; Luca Saba; Dinesh Kumar; Elisa Cuadrado Godia; Xiaoyi Jiang; Ajay Gupta; Petia Radeva; John R. Laird; Andrew Nicolaides; Jasjit S. Suri
	Title	A comparative approach of four different image registration techniques for quantitative assessment of coronary artery calcium lesions using intravascular ultrasound			Type	Journal Article
	Year	2015	Publication	Computer Methods and Programs in Biomedicine	Abbreviated Journal	CMPB
	Volume	118	Issue	2	Pages	158-172
	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	MILAB			Approved	no
	Call Number	Admin @ si @ AID2015			Serial	2640
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	Author	R.A.Bendezu; E.Barba; E.Burri; D.Cisternas; Carolina Malagelada; Santiago Segui; Anna Accarino; S.Quiroga; E.Monclus; I.Navazo
	Title	Intestinal gas content and distribution in health and in patients with functional gut symptoms			Type	Journal Article
	Year	2015	Publication	Neurogastroenterology & Motility	Abbreviated Journal	NEUMOT
	Volume	27	Issue	9	Pages	1249-1257
	Keywords
	Abstract	BACKGROUND: The precise relation of intestinal gas to symptoms, particularly abdominal bloating and distension remains incompletely elucidated. Our aim was to define the normal values of intestinal gas volume and distribution and to identify abnormalities in relation to functional-type symptoms. METHODS: Abdominal computed tomography scans were evaluated in healthy subjects (n = 37) and in patients in three conditions: basal (when they were feeling well; n = 88), during an episode of abdominal distension (n = 82) and after a challenge diet (n = 24). Intestinal gas content and distribution were measured by an original analysis program. Identification of patients outside the normal range was performed by machine learning techniques (one-class classifier). Results are expressed as median (IQR) or mean ± SE, as appropriate. KEY RESULTS: In healthy subjects the gut contained 95 (71, 141) mL gas distributed along the entire lumen. No differences were detected between patients studied under asymptomatic basal conditions and healthy subjects. However, either during a spontaneous bloating episode or once challenged with a flatulogenic diet, luminal gas was found to be increased and/or abnormally distributed in about one-fourth of the patients. These patients detected outside the normal range by the classifier exhibited a significantly greater number of abnormal features than those within the normal range (3.7 ± 0.4 vs 0.4 ± 0.1; p < 0.001). CONCLUSIONS & INFERENCES: The analysis of a large cohort of subjects using original techniques provides unique and heretofore unavailable information on the volume and distribution of intestinal gas in normal conditions and in relation to functional gastrointestinal symptoms.
	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	MILAB			Approved	no
	Call Number	Admin @ si @ BBB2015			Serial	2667
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	Author	G.Blasco; Simone Balocco; J.Puig; J.Sanchez-Gonzalez; W.Ricart; J.Daunis-I-Estadella; X.Molina; S.Pedraza; J.M.Fernandez-Real
	Title	Carotid pulse wave velocity by magnetic resonance imaging is increased in middle-aged subjects with the metabolic syndrome			Type	Journal Article
	Year	2015	Publication	International Journal of Cardiovascular Imaging	Abbreviated Journal	ICJI
	Volume	31	Issue	3	Pages	603-612
	Keywords	Metabolic syndrome; Arterial stiffness; Pulse wave velocity; Carotid artery; Magnetic resonance
	Abstract	Arterial pulse wave velocity (PWV), an independent predictor of cardiovascular disease, physiologically increases with age; however, growing evidence suggests metabolic syndrome (MetS) accelerates this increase. Magnetic resonance imaging (MRI) enables reliable noninvasive assessment of arterial stiffness by measuring arterial PWV in specific vascular segments. We investigated the association between the presence of MetS and its components with carotid PWV (cPWV) in asymptomatic subjects without diabetes. We assessed cPWV by MRI in 61 individuals (mean age, 55.3 ± 14.1 years; median age, 55 years): 30 with MetS and 31 controls with similar age, sex, body mass index, and LDL-cholesterol levels. The study population was dichotomized by the median age. To remove the physiological association between PWV and age, unpaired t tests and multiple regression analyses were performed using the residuals of the regression between PWV and age. cPWV was higher in middle-aged subjects with MetS than in those without (p = 0.001), but no differences were found in elder subjects (p = 0.313). cPWV was associated with diastolic blood pressure (r = 0.276, p = 0.033) and waist circumference (r = 0.268, p = 0.038). The presence of MetS was associated with increased cPWV regardless of age, sex, blood pressure, and waist (p = 0.007). The MetS components contributing independently to an increased cPWV were hypertension (p = 0.018) and hypertriglyceridemia (p = 0.002). The presence of MetS is associated with an increased cPWV in middle-aged subjects. In particular, hypertension and hypertriglyceridemia may contribute to early progression of carotid stiffness.
	Address
	Corporate Author				Thesis
	Publisher	Springer Netherlands	Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	1569-5794	ISBN		Medium
	Area		Expedition		Conference
	Notes	MILAB			Approved	no
	Call Number	Admin @ si @ BBP2015			Serial	2670
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	Author	Lluis Garrido; M.Guerrieri; Laura Igual
	Title	Image Segmentation with Cage Active Contours			Type	Journal Article
	Year	2015	Publication	IEEE Transactions on Image Processing	Abbreviated Journal	TIP
	Volume	24	Issue	12	Pages	5557 - 5566
	Keywords	Level sets; Mean value coordinates; Parametrized active contours; level sets; mean value coordinates
	Abstract	In this paper, we present a framework for image segmentation based on parametrized active contours. The evolving contour is parametrized according to a reduced set of control points that form a closed polygon and have a clear visual interpretation. The parametrization, called mean value coordinates, stems from the techniques used in computer graphics to animate virtual models. Our framework allows to easily formulate region-based energies to segment an image. In particular, we present three different local region-based energy terms: 1) the mean model; 2) the Gaussian model; 3) and the histogram model. We show the behavior of our method on synthetic and real images and compare the performance with state-of-the-art level set 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	1057-7149	ISBN		Medium
	Area		Expedition		Conference
	Notes	MILAB			Approved	no
	Call Number	Admin @ si @ GGI2015			Serial	2673
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	Author	Marta Nuñez-Garcia; Sonja Simpraga; M.Angeles Jurado; Maite Garolera; Roser Pueyo; Laura Igual
	Title	FADR: Functional-Anatomical Discriminative Regions for rest fMRI Characterization			Type	Conference Article
	Year	2015	Publication	Machine Learning in Medical Imaging, Proceedings of 6th International Workshop, MLMI 2015, Held in Conjunction with MICCAI 2015	Abbreviated Journal
	Volume		Issue		Pages	61-68
	Keywords
	Abstract
	Address	Munich; Germany; October 2015
	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	MLMI
	Notes	MILAB			Approved	no
	Call Number	Admin @ si @ NSJ2015			Serial	2674
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