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Author Marta Ligero; Guillermo Torres; Carles Sanchez; Katerine Diaz; Raquel Perez; Debora Gil edit  url
doi  openurl
  Title Selection of Radiomics Features based on their Reproducibility Type Conference Article
  Year 2019 Publication 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society Abbreviated Journal  
  Volume (down) Issue Pages 403-408  
  Keywords  
  Abstract Dimensionality reduction is key to alleviate machine learning artifacts in clinical applications with Small Sample Size (SSS) unbalanced datasets. Existing methods rely on either the probabilistic distribution of training data or the discriminant power of the reduced space, disregarding the impact of repeatability and uncertainty in features.In the present study is proposed the use of reproducibility of radiomics features to select features with high inter-class correlation coefficient (ICC). The reproducibility includes the variability introduced in the image acquisition, like medical scans acquisition parameters and convolution kernels, that affects intensity-based features and tumor annotations made by physicians, that influences morphological descriptors of the lesion.For the reproducibility of radiomics features three studies were conducted on cases collected at Vall Hebron Oncology Institute (VHIO) on responders to oncology treatment. The studies focused on the variability due to the convolution kernel, image acquisition parameters, and the inter-observer lesion identification. The features selected were those features with a ICC higher than 0.7 in the three studies.The selected features based on reproducibility were evaluated for lesion malignancy classification using a different database. Results show better performance compared to several state-of-the-art methods including Principal Component Analysis (PCA), Kernel Discriminant Analysis via QR decomposition (KDAQR), LASSO, and an own built Convolutional Neural Network.  
  Address Berlin; Alemanya; July 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 EMBC  
  Notes IAM; 600.139; 600.145 Approved no  
  Call Number Admin @ si @ LTS2019 Serial 3358  
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Author Carles Sanchez; Miguel Viñas; Coen Antens; Agnes Borras; Debora Gil edit  url
doi  openurl
  Title Back to Front Architecture for Diagnosis as a Service Type Conference Article
  Year 2018 Publication 20th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing Abbreviated Journal  
  Volume (down) Issue Pages 343-346  
  Keywords  
  Abstract Software as a Service (SaaS) is a cloud computing model in which a provider hosts applications in a server that customers use via internet. Since SaaS does not require to install applications on customers' own computers, it allows the use by multiple users of highly specialized software without extra expenses for hardware acquisition or licensing. A SaaS tailored for clinical needs not only would alleviate licensing costs, but also would facilitate easy access to new methods for diagnosis assistance. This paper presents a SaaS client-server architecture for Diagnosis as a Service (DaaS). The server is based on docker technology in order to allow execution of softwares implemented in different languages with the highest portability and scalability. The client is a content management system allowing the design of websites with multimedia content and interactive visualization of results allowing user editing. We explain a usage case that uses our DaaS as crowdsourcing platform in a multicentric pilot study carried out to evaluate the clinical benefits of a software for assessment of central airway obstruction.  
  Address Timisoara; Rumania; September 2018  
  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 SYNASC  
  Notes IAM; 600.145 Approved no  
  Call Number Admin @ si @ SVA2018 Serial 3360  
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Author Debora Gil; Antoni Rosell edit  openurl
  Title Advances in Artificial Intelligence – How Lung Cancer CT Screening Will Progress? Type Abstract
  Year 2019 Publication World Lung Cancer Conference Abbreviated Journal  
  Volume (down) Issue Pages  
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  Abstract Invited speaker  
  Address Barcelona; September 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 IASLC WCLC  
  Notes IAM; 600.139; 600.145 Approved no  
  Call Number Admin @ si @ GiR2019 Serial 3361  
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Author Antonio Esteban Lansaque edit  isbn
openurl 
  Title An Endoscopic Navigation System for Lung Cancer Biopsy Type Book Whole
  Year 2019 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal  
  Volume (down) Issue Pages  
  Keywords  
  Abstract Lung cancer is one of the most diagnosed cancers among men and women. Actually,
lung cancer accounts for 13% of the total cases with a 5-year global survival
rate in patients. Although Early detection increases survival rate from 38% to 67%, accurate diagnosis remains a challenge. Pathological confirmation requires extracting a sample of the lesion tissue for its biopsy. The preferred procedure for tissue biopsy is called bronchoscopy. A bronchoscopy is an endoscopic technique for the internal exploration of airways which facilitates the performance of minimal invasive interventions with low risk for the patient. Recent advances in bronchoscopic devices have increased their use for minimal invasive diagnostic and intervention procedures, like lung cancer biopsy sampling. Despite the improvement in bronchoscopic device quality, there is a lack of intelligent computational systems for supporting in-vivo clinical decision during examinations. Existing technologies fail to accurately reach the lesion due to several aspects at intervention off-line planning and poor intra-operative guidance at exploration time. Existing guiding systems radiate patients and clinical staff,might be expensive and achieve a suboptimlal 70% of yield boost. Diagnostic yield could be improved reducing radiation and costs by developing intra-operative support systems able to guide the bronchoscopist to the lesion during the intervention. The goal of this PhD thesis is to develop an image-based navigation systemfor intra-operative guidance of bronchoscopists to a target lesion across a path previously planned on a CT-scan. We propose a 3D navigation system which uses the anatomy of video bronchoscopy frames to locate the bronchoscope within the airways. Once the bronchoscope is located, our navigation system is able to indicate the bifurcation which needs to be followed to reach the lesion. In order to facilitate an off-line validation
as realistic as possible, we also present a method for augmenting simulated virtual bronchoscopies with the appearance of intra-operative videos. Experiments performed on augmented and intra-operative videos, prove that our algorithm can be speeded up for an on-line implementation in the operating room.
 
  Address October 2019  
  Corporate Author Thesis Ph.D. thesis  
  Publisher Ediciones Graficas Rey Place of Publication Editor Debora Gil; Carles Sanchez  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN 978-84-121011-0-2 Medium  
  Area Expedition Conference  
  Notes IAM; 600.139; 600.145 Approved no  
  Call Number Admin @ si @ Est2019 Serial 3392  
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Author Saad Minhas; Aura Hernandez-Sabate; Shoaib Ehsan; Klaus McDonald Maier edit  doi
openurl 
  Title Effects of Non-Driving Related Tasks during Self-Driving mode Type Journal Article
  Year 2020 Publication IEEE Transactions on Intelligent Transportation Systems Abbreviated Journal TITS  
  Volume (down) Issue Pages  
  Keywords  
  Abstract Perception reaction time and mental workload have proven to be crucial in manual driving. Moreover, in highly automated cars, where most of the research is focusing on Level 4 Autonomous driving, take-over performance is also a key factor when taking road safety into account. This study aims to investigate how the immersion in non-driving related tasks affects the take-over performance of drivers in given scenarios. The paper also highlights the use of virtual simulators to gather efficient data that can be crucial in easing the transition between manual and autonomous driving scenarios. The use of Computer Aided Simulations is of absolute importance in this day and age since the automotive industry is rapidly moving towards Autonomous technology. An experiment comprising of 40 subjects was performed to examine the reaction times of driver and the influence of other variables in the success of take-over performance in highly automated driving under different circumstances within a highway virtual environment. The results reflect the relationship between reaction times under different scenarios that the drivers might face under the circumstances stated above as well as the importance of variables such as velocity in the success on regaining car control after automated driving. The implications of the results acquired are important for understanding the criteria needed for designing Human Machine Interfaces specifically aimed towards automated driving conditions. Understanding the need to keep drivers in the loop during automation, whilst allowing drivers to safely engage in other non-driving related tasks is an important research area which can be aided by the proposed study.  
  Address  
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  Notes IAM; 600.139; 600.145 Approved no  
  Call Number Admin @ si @ MHE2020 Serial 3468  
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Author Angels Barbera; Ruth Marginet Flinch; Montserrat Martin; Jose L Mate; Albert Oriol; Fina Martinez-Soler; Tomas Santalucia; Pedro L. Fernandez edit  url
doi  openurl
  Title The Immunohistochemical Expression of Programmed Death Ligand 1 Type Journal Article
  Year 2020 Publication Applied Immunohistochemistry & Molecular Morphology Abbreviated Journal AIMM  
  Volume (down) Issue Pages  
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  Abstract Humanized antibodies targeting programmed death receptor 1 (PD-1) or its ligand (PD-L1) have been approved for the treatment of different cancers. Some of these antibodies show a correlation between the tissue expression of PD-L1 and response. Evaluation of PD-L1 expression presents multiple challenges, but some preanalytical issues such as tissue fixation have been scarcely evaluated. With the hypothesis that immunohistochemical staining of PD-L1 may be impacted by the time of specimen fixation, we evaluated differences in its expression in tonsil samples exposed to predefined fixation times. Random nontumoral tonsillectomy specimens were blindly evaluated in tissue microarray slides after staining with SP142 and SP263 antibodies. With fixation times ranging from 12 to 72 hours, between 2.8% and 6.1% of the samples were considered to be suboptimally stained, with no differences between the 2 antibodies within these fixation times. A significantly higher proportion of samples exposed to a fixation time of 96 hours presented suboptimal immunostaining (15.6%, P<0.0001). In addition, suboptimally stained spots were 20.8% using SP142 and 10.4% using SP263 after 96 hours of fixation (P=0.046). In conclusion, the quality of staining for PD-L1 in tonsil samples decreased with overfixation of the specimen at times >72 hours. Samples exposed to formaldehyde for longer periods presented suboptimal results for both clones, but the SP142 antibody presented a significantly lower tolerance to formalin overexposure than SP263. These results indicate the relevance of a controlled preanalytical processing of samples and particularly the length of fixation of tumor specimens.  
  Address  
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  Notes IAM; 600.139; 600.145 Approved no  
  Call Number Admin @ si @ BMM2020 Serial 3470  
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Author Debora Gil; Guillermo Torres edit  openurl
  Title A multi-shape loss function with adaptive class balancing for the segmentation of lung structures Type Conference Article
  Year 2020 Publication 34th International Congress and Exhibition on Computer Assisted Radiology & Surgery Abbreviated Journal  
  Volume (down) Issue Pages  
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  Abstract  
  Address Virtual; June 2020  
  Corporate Author Thesis  
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  Language Summary Language Original Title  
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  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference CARS  
  Notes IAM; 600.139; 600.145 Approved no  
  Call Number Admin @ si @ GiT2020 Serial 3472  
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Author Debora Gil; Oriol Ramos Terrades; Raquel Perez edit  openurl
  Title Topological Radiomics (TOPiomics): Early Detection of Genetic Abnormalities in Cancer Treatment Evolution Type Conference Article
  Year 2020 Publication Women in Geometry and Topology Abbreviated Journal  
  Volume (down) Issue Pages  
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  Abstract  
  Address Barcelona; September 2019  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
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  Area Expedition Conference  
  Notes IAM; DAG; 600.139; 600.145; 600.121 Approved no  
  Call Number Admin @ si @ GRP2020 Serial 3473  
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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 (down) Issue Pages  
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  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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  Corporate Author Thesis  
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  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
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  ISSN ISBN Medium  
  Area Expedition Conference  
  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 (down) 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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  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 Medium  
  Area Expedition Conference  
  Notes IAM; DAG; 600.139; 600.145; 600.140; 600.121 Approved no  
  Call Number Admin @ si @ RBG2020 Serial 3475  
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