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Author Debora Gil; Katerine Diaz; Carles Sanchez; Aura Hernandez-Sabate edit   pdf
url  openurl
  Title Early Screening of SARS-CoV-2 by Intelligent Analysis of X-Ray Images Type Miscellaneous
  Year 2020 Publication Arxiv Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract Future SARS-CoV-2 virus outbreak COVID-XX might possibly occur during the next years. However the pathology in humans is so recent that many clinical aspects, like early detection of complications, side effects after recovery or early screening, are currently unknown. In spite of the number of cases of COVID-19, its rapid spread putting many sanitary systems in the edge of collapse has hindered proper collection and analysis of the data related to COVID-19 clinical aspects. We describe an interdisciplinary initiative that integrates clinical research, with image diagnostics and the use of new technologies such as artificial intelligence and radiomics with the aim of clarifying some of SARS-CoV-2 open questions. The whole initiative addresses 3 main points: 1) collection of standardize data including images, clinical data and analytics; 2) COVID-19 screening for its early diagnosis at primary care centers; 3) define radiomic signatures of COVID-19 evolution and associated pathologies for the early treatment of complications. In particular, in this paper we present a general overview of the project, the experimental design and first results of X-ray COVID-19 detection using a classic approach based on HoG and feature selection. Our experiments include a comparison to some recent methods for COVID-19 screening in X-Ray and an exploratory analysis of the feasibility of X-Ray COVID-19 screening. Results show that classic approaches can outperform deep-learning methods in this experimental setting, indicate the feasibility of early COVID-19 screening and that non-COVID infiltration is the group of patients most similar to COVID-19 in terms of radiological description of X-ray. Therefore, an efficient COVID-19 screening should be complemented with other clinical data to better discriminate these cases.  
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  Notes IAM; 600.139; 600.145; 601.337 Approved no  
  Call Number Admin @ si @ GDS2020 Serial 3474  
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Author Oriol Ramos Terrades; Albert Berenguel; Debora Gil edit   pdf
url  openurl
  Title A flexible outlier detector based on a topology given by graph communities Type Miscellaneous
  Year 2020 Publication Arxiv Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract Outlier, or anomaly, detection is essential for optimal performance of machine learning methods and statistical predictive models. It is not just a technical step in a data cleaning process but a key topic in many fields such as fraudulent document detection, in medical applications and assisted diagnosis systems or detecting security threats. In contrast to population-based methods, neighborhood based local approaches are simple flexible methods that have the potential to perform well in small sample size unbalanced problems. However, a main concern of local approaches is the impact that the computation of each sample neighborhood has on the method performance. Most approaches use a distance in the feature space to define a single neighborhood that requires careful selection of several parameters. This work presents a local approach based on a local measure of the heterogeneity of sample labels in the feature space considered as a topological manifold. Topology is computed using the communities of a weighted graph codifying mutual nearest neighbors in the feature space. This way, we provide with a set of multiple neighborhoods able to describe the structure of complex spaces without parameter fine tuning. The extensive experiments on real-world data sets show that our approach overall outperforms, both, local and global strategies in multi and single view settings.  
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  Notes IAM; DAG; 600.139; 600.145; 600.140; 600.121 Approved no  
  Call Number Admin @ si @ RBG2020 Serial 3475  
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Author Teresa Moran; Vanesa Quiroga; Beatriz Cirauqui; Laia Vila; Maria Gil-Moreno; Enric Carcereny; Mireia Margeli; Ana Muñoz-Marmol; Jose Luis Mate; Jose Maria Velarde; Miguel Angel Molina; Rafael Rosell edit  url
doi  openurl
  Title A Single-Center Retrospective Study of Patients with Double Primary Cancers: Breast Cancer and EGFR-Mutant Non-Small Cell Lung Cancer Type Journal Article
  Year 2019 Publication Oncology Research and Treatment Abbreviated Journal  
  Volume 42 Issue 3 Pages 107-114  
  Keywords  
  Abstract Background: Second primary malignancies (SPM) in the lung are not common in breast cancer (BC) patients. EGFR-mutant lung cancer (LC) is a separate molecular subset, and the co-existence of EGFR-mutant LC and BC has not been explored. We hypothesized that EGFR-mutant LC patients could have higher rates of primary BC than those with EGFR-wild type (WT).

Methods: We collected data on clinical and molecular characteristics and outcomes of female patients with LC and a previous or simultaneous history of primary BC treated in our hospital from 2008 to 2014.

Results: Data on treatment, follow-up, and EGFR mutation status were available for 356 patients. 17.7% (11/62) of patients with EGFR mutations had BC, compared to 1.02% (3/294) of EGFR-WT patients (p < 0.001). Both tumors were metachronous in 81.8%, with LC diagnosed 9 years after the diagnosis of BC. 5 of the 6 (83.3%) BC patients treated with radiotherapy developed LC in an area within the radiation field. No EGFR mutations were detected in BC tissue and no HER2 expression was detected in LC samples.

Conclusion: SPM in the lung and breast occur more frequently among EGFR-mutant compared to EGFR-WT LC patients. Radiotherapy for BC may increase the risk of developing primary LC.
 
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  Area Expedition Conference  
  Notes IAM; 600.139; 600.145 Approved no  
  Call Number Admin @ si @ MQC2019 Serial 3476  
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Author Esmitt Ramirez; Carles Sanchez; Debora Gil edit   pdf
url  doi
openurl 
  Title Localizing Pulmonary Lesions Using Fuzzy Deep Learning Type Conference Article
  Year 2019 Publication 21st International Symposium on Symbolic and Numeric Algorithms for Scientific Computing Abbreviated Journal  
  Volume Issue Pages 290-294  
  Keywords  
  Abstract The usage of medical images is part of the clinical daily in several healthcare centers around the world. Particularly, Computer Tomography (CT) images are an important key in the early detection of suspicious lung lesions. The CT image exploration allows the detection of lung lesions before any invasive procedure (e.g. bronchoscopy, biopsy). The effective localization of lesions is performed using different image processing and computer vision techniques. Lately, the usage of deep learning models into medical imaging from detection to prediction shown that is a powerful tool for Computer-aided software. In this paper, we present an approach to localize pulmonary lung lesion using fuzzy deep learning. Our approach uses a simple convolutional neural network based using the LIDC-IDRI dataset. Each image is divided into patches associated a probability vector (fuzzy) according their belonging to anatomical structures on a CT. We showcase our approach as part of a full CAD system to exploration, planning, guiding and detection of pulmonary lesions.  
  Address Timisoara; Rumania; September 2019  
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  Area Expedition Conference SYNASC  
  Notes IAM; 600.145; 600.140; 601.337; 601.323 Approved no  
  Call Number Admin @ si @ RSG2019 Serial 3531  
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Author Debora Gil; Aura Hernandez-Sabate; Mireia Brunat;Steven Jansen; Jordi Martinez-Vilalta edit   pdf
doi  openurl
  Title Structure-preserving smoothing of biomedical images Type Journal Article
  Year 2011 Publication Pattern Recognition Abbreviated Journal PR  
  Volume 44 Issue 9 Pages 1842-1851  
  Keywords Non-linear smoothing; Differential geometry; Anatomical structures; segmentation; Cardiac magnetic resonance; Computerized tomography  
  Abstract Smoothing of biomedical images should preserve gray-level transitions between adjacent tissues, while restoring contours consistent with anatomical structures. Anisotropic diffusion operators are based on image appearance discontinuities (either local or contextual) and might fail at weak inter-tissue transitions. Meanwhile, the output of block-wise and morphological operations is prone to present a block structure due to the shape and size of the considered pixel neighborhood. In this contribution, we use differential geometry concepts to define a diffusion operator that restricts to image consistent level-sets. In this manner, the final state is a non-uniform intensity image presenting homogeneous inter-tissue transitions along anatomical structures, while smoothing intra-structure texture. Experiments on different types of medical images (magnetic resonance, computerized tomography) illustrate its benefit on a further process (such as segmentation) of images.  
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  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN (up) 0031-3203 ISBN Medium  
  Area Expedition Conference  
  Notes IAM; ADAS Approved no  
  Call Number IAM @ iam @ GHB2011 Serial 1526  
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Author David Roche; Debora Gil; Jesus Giraldo edit  doi
isbn  openurl
  Title Mathematical modeling of G protein-coupled receptor function: What can we learn from empirical and mechanistic models? Type Book Chapter
  Year 2014 Publication G Protein-Coupled Receptors – Modeling and Simulation Advances in Experimental Medicine and Biology Abbreviated Journal  
  Volume 796 Issue 3 Pages 159-181  
  Keywords β-arrestin; biased agonism; curve fitting; empirical modeling; evolutionary algorithm; functional selectivity; G protein; GPCR; Hill coefficient; intrinsic efficacy; inverse agonism; mathematical modeling; mechanistic modeling; operational model; parameter optimization; receptor dimer; receptor oligomerization; receptor constitutive activity; signal transduction; two-state model  
  Abstract Empirical and mechanistic models differ in their approaches to the analysis of pharmacological effect. Whereas the parameters of the former are not physical constants those of the latter embody the nature, often complex, of biology. Empirical models are exclusively used for curve fitting, merely to characterize the shape of the E/[A] curves. Mechanistic models, on the contrary, enable the examination of mechanistic hypotheses by parameter simulation. Regretfully, the many parameters that mechanistic models may include can represent a great difficulty for curve fitting, representing, thus, a challenge for computational method development. In the present study some empirical and mechanistic models are shown and the connections, which may appear in a number of cases between them, are analyzed from the curves they yield. It may be concluded that systematic and careful curve shape analysis can be extremely useful for the understanding of receptor function, ligand classification and drug discovery, thus providing a common language for the communication between pharmacologists and medicinal chemists.  
  Address  
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  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 (up) 0065-2598 ISBN 978-94-007-7422-3 Medium  
  Area Expedition Conference  
  Notes IAM; 600.075 Approved no  
  Call Number IAM @ iam @ RGG2014 Serial 2197  
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Author Enric Marti; Jordi Regincos;Jaime Lopez-Krahe; Juan J.Villanueva edit  url
doi  openurl
  Title Hand line drawing interpretation as three-dimensional objects Type Journal Article
  Year 1993 Publication Signal Processing – Intelligent systems for signal and image understanding Abbreviated Journal  
  Volume 32 Issue 1-2 Pages 91-110  
  Keywords Line drawing interpretation; line labelling; scene analysis; man-machine interaction; CAD input; line extraction  
  Abstract In this paper we present a technique to interpret hand line drawings as objects in a three-dimensional space. The object domain considered is based on planar surfaces with straight edges, concretely, on ansextension of Origami world to hidden lines. The line drawing represents the object under orthographic projection and it is sensed using a scanner. Our method is structured in two modules: feature extraction and feature interpretation. In the first one, image processing techniques are applied under certain tolerance margins to detect lines and junctions on the hand line drawing. Feature interpretation module is founded on line labelling techniques using a labelled junction dictionary. A labelling algorithm is here proposed. It uses relaxation techniques to reduce the number of incompatible labels with the junction dictionary so that the convergence of solutions can be accelerated. We formulate some labelling hypotheses tending to eliminate elements in two sets of labelled interpretations. That is, those which are compatible with the dictionary but do not correspond to three-dimensional objects and those which represent objects not very probable to be specified by means of a line drawing. New entities arise on the line drawing as a result of the extension of Origami world. These are defined to enunciate the assumptions of our method as well as to clarify the algorithms proposed. This technique is framed in a project aimed to implement a system to create 3D objects to improve man-machine interaction in CAD systems.  
  Address  
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  Publisher Elsevier North-Holland, Inc. Place of Publication Amsterdam, The Netherlands, The Netherlands Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN (up) 0165-1684 ISBN Medium  
  Area Expedition Conference  
  Notes IAM;ISE; Approved no  
  Call Number IAM @ iam @ MRL1993 Serial 1611  
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Author Debora Gil; Petia Radeva edit   pdf
doi  openurl
  Title Inhibition of false landmarks Type Journal Article
  Year 2006 Publication Pattern Recognition Letters Abbreviated Journal PRL  
  Volume 27 Issue 9 Pages 1022-1030  
  Keywords  
  Abstract Corners and junctions are landmarks characterized by the lack of differentiability in the unit tangent to the image level curve. Detectors based on differential operators are not, by their own definition, the best posed as they require a higher degree of differentiability to yield a reliable response. We argue that a corner detector should be based on the degree of continuity of the tangent vector to the image level sets, work on the image domain and need no assumptions on neither the image local structure nor the particular geometry of the corner/junction. An operator measuring the degree of differentiability of the projection matrix on the image gradient fulfills the above requirements. Because using smoothing kernels leads to corner misplacement, we suggest an alternative fake response remover based on the receptive field inhibition of spurious details. The combination of both orientation discontinuity detection and noise inhibition produce our inhibition orientation energy (IOE) landmark locator.  
  Address  
  Corporate Author Thesis  
  Publisher Elsevier Science Inc. Place of Publication New York, NY, USA Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN (up) 0167-8655 ISBN Medium  
  Area Expedition Conference  
  Notes IAM;MILAB Approved no  
  Call Number IAM @ iam @ GiR2006 Serial 1529  
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Author Ernest Valveny; Enric Marti edit   pdf
doi  openurl
  Title A model for image generation and symbol recognition through the deformation of lineal shapes Type Journal Article
  Year 2003 Publication Pattern Recognition Letters Abbreviated Journal PRL  
  Volume 24 Issue 15 Pages 2857-2867  
  Keywords  
  Abstract We describe a general framework for the recognition of distorted images of lineal shapes, which relies on three items: a model to represent lineal shapes and their deformations, a model for the generation of distorted binary images and the combination of both models in a common probabilistic framework, where the generation of deformations is related to an internal energy, and the generation of binary images to an external energy. Then, recognition consists in the minimization of a global energy function, performed by using the EM algorithm. This general framework has been applied to the recognition of hand-drawn lineal symbols in graphic documents.  
  Address  
  Corporate Author Thesis  
  Publisher Elsevier Science Inc. Place of Publication New York, NY, USA Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN (up) 0167-8655 ISBN Medium  
  Area Expedition Conference  
  Notes DAG; IAM Approved no  
  Call Number IAM @ iam @ VAM2003 Serial 1653  
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Author Oriol Pujol; Debora Gil; Petia Radeva edit   pdf
doi  openurl
  Title Fundamentals of Stop and Go active models Type Journal Article
  Year 2005 Publication Image and Vision Computing Abbreviated Journal  
  Volume 23 Issue 8 Pages 681-691  
  Keywords Deformable models; Geodesic snakes; Region-based segmentation  
  Abstract An efficient snake formulation should conform to the idea of picking the smoothest curve among all the shapes approximating an object of interest. In current geodesic snakes, the regularizing curvature also affects the convergence stage, hindering the latter at concave regions. In the present work, we make use of characteristic functions to define a novel geodesic formulation that decouples regularity and convergence. This term decoupling endows the snake with higher adaptability to non-convex shapes. Convergence is ensured by splitting the definition of the external force into an attractive vector field and a repulsive one. In our paper, we propose to use likelihood maps as approximation of characteristic functions of object appearance. The better efficiency and accuracy of our decoupled scheme are illustrated in the particular case of feature space-based segmentation.  
  Address  
  Corporate Author Thesis  
  Publisher Butterworth-Heinemann Place of Publication Newton, MA, USA Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN (up) 0262-8856 ISBN Medium  
  Area Expedition Conference  
  Notes IAM;MILAB;HuPBA Approved no  
  Call Number IAM @ iam @ PGR2005 Serial 1629  
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