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Author Mireia Sole; Joan Blanco; Debora Gil; G. Fonseka; Richard Frodsham; Oliver Valero; Francesca Vidal; Zaida Sarrate edit   pdf
openurl 
  Title Is there a pattern of Chromosome territoriality along mice spermatogenesis? Type Conference Article
  Year 2017 Publication 3rd Spanish MeioNet Meeting Abstract Book Abbreviated Journal  
  Volume Issue Pages 55-56  
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  Abstract  
  Address Miraflores de la Sierra; Madrid; June 2017  
  Corporate Author Thesis  
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  ISSN ISBN (up) Medium  
  Area Expedition Conference MEIONET  
  Notes IAM; 600.096; 600.145 Approved no  
  Call Number Admin @ si @ Serial 2958  
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Author Mireia Sole; Joan Blanco; Debora Gil; G. Fonseka; Richard Frodsham; Oliver Valero; Francesca Vidal; Zaida Sarrate edit  openurl
  Title Unraveling the enigmas of chromosome territoriality during spermatogenesis Type Conference Article
  Year 2017 Publication IX Jornada del Departament de Biologia Cel•lular, Fisiologia i Immunologia Abbreviated Journal  
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  Abstract  
  Address UAB; Barcelona; June 2017  
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  Language Summary Language Original Title  
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  Area Expedition Conference  
  Notes IAM; 600.145 Approved no  
  Call Number Admin @ si @ SBG2017b Serial 2959  
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Author Pau Cano; Alvaro Caravaca; Debora Gil; Eva Musulen edit   pdf
url  openurl
  Title Diagnosis of Helicobacter pylori using AutoEncoders for the Detection of Anomalous Staining Patterns in Immunohistochemistry Images Type Miscellaneous
  Year 2023 Publication Arxiv Abbreviated Journal  
  Volume Issue Pages 107241  
  Keywords  
  Abstract This work addresses the detection of Helicobacter pylori a bacterium classified since 1994 as class 1 carcinogen to humans. By its highest specificity and sensitivity, the preferred diagnosis technique is the analysis of histological images with immunohistochemical staining, a process in which certain stained antibodies bind to antigens of the biological element of interest. This analysis is a time demanding task, which is currently done by an expert pathologist that visually inspects the digitized samples.
We propose to use autoencoders to learn latent patterns of healthy tissue and detect H. pylori as an anomaly in image staining. Unlike existing classification approaches, an autoencoder is able to learn patterns in an unsupervised manner (without the need of image annotations) with high performance. In particular, our model has an overall 91% of accuracy with 86\% sensitivity, 96% specificity and 0.97 AUC in the detection of H. pylori.
 
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  Area Expedition Conference  
  Notes IAM Approved no  
  Call Number Admin @ si @ CCG2023 Serial 3855  
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Author Katerine Diaz; Jesus Martinez del Rincon; Aura Hernandez-Sabate edit   pdf
url  openurl
  Title Decremental generalized discriminative common vectors applied to images classification Type Journal Article
  Year 2017 Publication Knowledge-Based Systems Abbreviated Journal KBS  
  Volume 131 Issue Pages 46-57  
  Keywords Decremental learning; Generalized Discriminative Common Vectors; Feature extraction; Linear subspace methods; Classification  
  Abstract In this paper, a novel decremental subspace-based learning method called Decremental Generalized Discriminative Common Vectors method (DGDCV) is presented. The method makes use of the concept of decremental learning, which we introduce in the field of supervised feature extraction and classification. By efficiently removing unnecessary data and/or classes for a knowledge base, our methodology is able to update the model without recalculating the full projection or accessing to the previously processed training data, while retaining the previously acquired knowledge. The proposed method has been validated in 6 standard face recognition datasets, showing a considerable computational gain without compromising the accuracy of the model.  
  Address  
  Corporate Author Thesis  
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  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN (up) Medium  
  Area Expedition Conference  
  Notes ADAS; 600.118; 600.121;IAM Approved no  
  Call Number Admin @ si @ DMH2017a Serial 3003  
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Author Mireia Sole; Joan Blanco; Debora Gil; G. Fonseka; Richard Frodsham; Oliver Valero; Francesca Vidal; Zaida Sarrate edit  openurl
  Title Análisis 3d de la territorialidad cromosómica en células espermatogénicas: explorando la infertilidad desde un nuevo prisma Type Journal
  Year 2017 Publication Revista Asociación para el Estudio de la Biología de la Reproducción Abbreviated Journal ASEBIR  
  Volume 22 Issue 2 Pages 105  
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  Address  
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  ISSN ISBN (up) Medium  
  Area Expedition Conference  
  Notes IAM; 600.096; 600.145 Approved no  
  Call Number Admin @ si @ SBG2017d Serial 3042  
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Author Debora Gil; Rosa Maria Ortiz; Carles Sanchez; Antoni Rosell edit   pdf
doi  openurl
  Title Objective endoscopic measurements of central airway stenosis. A pilot study Type Journal Article
  Year 2018 Publication Respiration Abbreviated Journal RES  
  Volume 95 Issue Pages 63–69  
  Keywords Bronchoscopy; Tracheal stenosis; Airway stenosis; Computer-assisted analysis  
  Abstract Endoscopic estimation of the degree of stenosis in central airway obstruction is subjective and highly variable. Objective: To determine the benefits of using SENSA (System for Endoscopic Stenosis Assessment), an image-based computational software, for obtaining objective stenosis index (SI) measurements among a group of expert bronchoscopists and general pulmonologists. Methods: A total of 7 expert bronchoscopists and 7 general pulmonologists were enrolled to validate SENSA usage. The SI obtained by the physicians and by SENSA were compared with a reference SI to set their precision in SI computation. We used SENSA to efficiently obtain this reference SI in 11 selected cases of benign stenosis. A Web platform with three user-friendly microtasks was designed to gather the data. The users had to visually estimate the SI from videos with and without contours of the normal and the obstructed area provided by SENSA. The users were able to modify the SENSA contours to define the reference SI using morphometric bronchoscopy. Results: Visual SI estimation accuracy was associated with neither bronchoscopic experience (p = 0.71) nor the contours of the normal and the obstructed area provided by the system (p = 0.13). The precision of the SI by SENSA was 97.7% (95% CI: 92.4-103.7), which is significantly better than the precision of the SI by visual estimation (p < 0.001), with an improvement by at least 15%. Conclusion: SENSA provides objective SI measurements with a precision of up to 99.5%, which can be calculated from any bronchoscope using an affordable scalable interface. Providing normal and obstructed contours on bronchoscopic videos does not improve physicians' visual estimation of the SI.  
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  Area Expedition Conference  
  Notes IAM; 600.075; 600.096; 600.145 Approved no  
  Call Number Admin @ si @ GOS2018 Serial 3043  
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Author Rosa Maria Ortiz; Debora Gil; Elisa Minchole; Marta Diez-Ferrer; Noelia Cubero de Frutos edit   pdf
doi  openurl
  Title Classification of Confolcal Endomicroscopy Patterns for Diagnosis of Lung Cancer Type Conference Article
  Year 2017 Publication 18th World Conference on Lung Cancer Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract Confocal Laser Endomicroscopy (CLE) is an emerging imaging technique that allows the in-vivo acquisition of cell patterns of potentially malignant lesions. Such patterns could discriminate between inflammatory and neoplastic lesions and, thus, serve as a first in-vivo biopsy to discard cases that do not actually require a cell biopsy.

The goal of this work is to explore whether CLE images obtained during videobronchoscopy contain enough visual information to discriminate between benign and malign peripheral lesions for lung cancer diagnosis. To do so, we have performed a pilot comparative study with 12 patients (6 adenocarcinoma and 6 benign-inflammatory) using 2 different methods for CLE pattern analysis: visual analysis by 3 experts and a novel methodology that uses graph methods to find patterns in pre-trained feature spaces. Our preliminary results indicate that although visual analysis can only achieve a 60.2% of accuracy, the accuracy of the proposed unsupervised image pattern classification raises to 84.6%.

We conclude that CLE images visual information allow in-vivo detection of neoplastic lesions and graph structural analysis applied to deep-learning feature spaces can achieve competitive results.
 
  Address Yokohama; Japan; October 2017  
  Corporate Author Thesis  
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  ISSN ISBN (up) Medium  
  Area Expedition Conference IASLC WCLC  
  Notes IAM; 600.096; 600.075; 600.145 Approved no  
  Call Number Admin @ si @ OGM2017 Serial 3044  
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Author Debora Gil; Aura Hernandez-Sabate; David Castells; Jordi Carrabina edit   pdf
doi  openurl
  Title CYBERH: Cyber-Physical Systems in Health for Personalized Assistance Type Conference Article
  Year 2017 Publication International Symposium on Symbolic and Numeric Algorithms for Scientific Computing Abbreviated Journal  
  Volume Issue Pages  
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  Abstract Assistance systems for e-Health applications have some specific requirements that demand of new methods for data gathering, analysis and modeling able to deal with SmallData:
1) systems should dynamically collect data from, both, the environment and the user to issue personalized recommendations; 2) data analysis should be able to tackle a limited number of samples prone to include non-informative data and possibly evolving in time due to changes in patient condition; 3) algorithms should run in real time with possibly limited computational resources and fluctuant internet access.
Electronic medical devices (and CyberPhysical devices in general) can enhance the process of data gathering and analysis in several ways: (i) acquiring simultaneously multiple sensors data instead of single magnitudes (ii) filtering data; (iii) providing real-time implementations condition by isolating tasks in individual processors of multiprocessors Systems-on-chip (MPSoC) platforms and (iv) combining information through sensor fusion
techniques.
Our approach focus on both aspects of the complementary role of CyberPhysical devices and analysis of SmallData in the process of personalized models building for e-Health applications. In particular, we will address the design of Cyber-Physical Systems in Health for Personalized Assistance (CyberHealth) in two specific application cases: 1) A Smart Assisted Driving System (SADs) for dynamical assessment of the driving capabilities of Mild Cognitive Impaired (MCI) people; 2) An Intelligent Operating Room (iOR) for improving the yield of bronchoscopic interventions for in-vivo lung cancer diagnosis.
 
  Address Timisoara; Rumania; September 2017  
  Corporate Author Thesis  
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  ISSN ISBN (up) Medium  
  Area Expedition Conference SYNASC  
  Notes IAM; 600.085; 600.096; 600.075; 600.145 Approved no  
  Call Number Admin @ si @ GHC2017 Serial 3045  
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Author Katerine Diaz; Jesus Martinez del Rincon; Aura Hernandez-Sabate; Marçal Rusiñol; Francesc J. Ferri edit   pdf
doi  openurl
  Title Fast Kernel Generalized Discriminative Common Vectors for Feature Extraction Type Journal Article
  Year 2018 Publication Journal of Mathematical Imaging and Vision Abbreviated Journal JMIV  
  Volume 60 Issue 4 Pages 512-524  
  Keywords  
  Abstract This paper presents a supervised subspace learning method called Kernel Generalized Discriminative Common Vectors (KGDCV), as a novel extension of the known Discriminative Common Vectors method with Kernels. Our method combines the advantages of kernel methods to model complex data and solve nonlinear
problems with moderate computational complexity, with the better generalization properties of generalized approaches for large dimensional data. These attractive combination makes KGDCV specially suited for feature extraction and classification in computer vision, image processing and pattern recognition applications. Two different approaches to this generalization are proposed, a first one based on the kernel trick (KT) and a second one based on the nonlinear projection trick (NPT) for even higher efficiency. Both methodologies
have been validated on four different image datasets containing faces, objects and handwritten digits, and compared against well known non-linear state-of-art methods. Results show better discriminant properties than other generalized approaches both linear or kernel. In addition, the KGDCV-NPT approach presents a considerable computational gain, without compromising the accuracy of the model.
 
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  Area Expedition Conference  
  Notes DAG; ADAS; 600.086; 600.130; 600.121; 600.118; 600.129;IAM Approved no  
  Call Number Admin @ si @ DMH2018a Serial 3062  
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Author Katerine Diaz; Francesc J. Ferri; Aura Hernandez-Sabate edit   pdf
url  doi
openurl 
  Title An overview of incremental feature extraction methods based on linear subspaces Type Journal Article
  Year 2018 Publication Knowledge-Based Systems Abbreviated Journal KBS  
  Volume 145 Issue Pages 219-235  
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  Abstract With the massive explosion of machine learning in our day-to-day life, incremental and adaptive learning has become a major topic, crucial to keep up-to-date and improve classification models and their corresponding feature extraction processes. This paper presents a categorized overview of incremental feature extraction based on linear subspace methods which aim at incorporating new information to the already acquired knowledge without accessing previous data. Specifically, this paper focuses on those linear dimensionality reduction methods with orthogonal matrix constraints based on global loss function, due to the extensive use of their batch approaches versus other linear alternatives. Thus, we cover the approaches derived from Principal Components Analysis, Linear Discriminative Analysis and Discriminative Common Vector methods. For each basic method, its incremental approaches are differentiated according to the subspace model and matrix decomposition involved in the updating process. Besides this categorization, several updating strategies are distinguished according to the amount of data used to update and to the fact of considering a static or dynamic number of classes. Moreover, the specific role of the size/dimension ratio in each method is considered. Finally, computational complexity, experimental setup and the accuracy rates according to published results are compiled and analyzed, and an empirical evaluation is done to compare the best approach of each kind.  
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  Series Volume Series Issue Edition  
  ISSN 0950-7051 ISBN (up) Medium  
  Area Expedition Conference  
  Notes ADAS; 600.118;IAM Approved no  
  Call Number Admin @ si @ DFH2018 Serial 3090  
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