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Author |
Marta Ligero; Guillermo Torres; Carles Sanchez; Katerine Diaz; Raquel Perez; Debora Gil |
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Title |
Selection of Radiomics Features based on their Reproducibility |
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Conference Article |
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2019 |
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41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society |
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403-408 |
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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. |
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Berlin; Alemanya; July 2019 |
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EMBC |
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IAM; 600.139; 600.145 |
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Admin @ si @ LTS2019 |
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3358 |
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Author |
Carles Sanchez; Miguel Viñas; Coen Antens; Agnes Borras; Debora Gil |
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Title |
Back to Front Architecture for Diagnosis as a Service |
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2018 |
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20th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing |
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343-346 |
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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. |
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Timisoara; Rumania; September 2018 |
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SYNASC |
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IAM; 600.145 |
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Admin @ si @ SVA2018 |
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3360 |
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Debora Gil; Antoni Rosell |
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Advances in Artificial Intelligence – How Lung Cancer CT Screening Will Progress? |
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2019 |
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World Lung Cancer Conference |
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Barcelona; September 2019 |
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IASLC WCLC |
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IAM; 600.139; 600.145 |
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Admin @ si @ GiR2019 |
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3361 |
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Author |
Antonio Esteban Lansaque |
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An Endoscopic Navigation System for Lung Cancer Biopsy |
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2019 |
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PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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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. |
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October 2019 |
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Ph.D. thesis |
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Ediciones Graficas Rey |
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Debora Gil; Carles Sanchez |
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978-84-121011-0-2 |
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IAM; 600.139; 600.145 |
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Admin @ si @ Est2019 |
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3392 |
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Author |
Saad Minhas; Aura Hernandez-Sabate; Shoaib Ehsan; Klaus McDonald Maier |
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Title |
Effects of Non-Driving Related Tasks during Self-Driving mode |
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2020 |
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IEEE Transactions on Intelligent Transportation Systems |
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TITS |
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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. |
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IAM; 600.139; 600.145 |
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Admin @ si @ MHE2020 |
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3468 |
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Angels Barbera; Ruth Marginet Flinch; Montserrat Martin; Jose L Mate; Albert Oriol; Fina Martinez-Soler; Tomas Santalucia; Pedro L. Fernandez |
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The Immunohistochemical Expression of Programmed Death Ligand 1 |
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2020 |
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Applied Immunohistochemistry & Molecular Morphology |
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AIMM |
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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. |
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IAM; 600.139; 600.145 |
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Admin @ si @ BMM2020 |
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3470 |
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Author |
Debora Gil; Guillermo Torres |
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A multi-shape loss function with adaptive class balancing for the segmentation of lung structures |
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2020 |
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34th International Congress and Exhibition on Computer Assisted Radiology & Surgery |
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Virtual; June 2020 |
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CARS |
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IAM; 600.139; 600.145 |
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Admin @ si @ GiT2020 |
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3472 |
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Debora Gil; Oriol Ramos Terrades; Raquel Perez |
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Topological Radiomics (TOPiomics): Early Detection of Genetic Abnormalities in Cancer Treatment Evolution |
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2020 |
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Women in Geometry and Topology |
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Barcelona; September 2019 |
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IAM; DAG; 600.139; 600.145; 600.121 |
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Admin @ si @ GRP2020 |
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3473 |
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Debora Gil; Katerine Diaz; Carles Sanchez; Aura Hernandez-Sabate |
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Early Screening of SARS-CoV-2 by Intelligent Analysis of X-Ray Images |
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2020 |
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Arxiv |
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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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IAM; 600.139; 600.145; 601.337 |
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Admin @ si @ GDS2020 |
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3474 |
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Oriol Ramos Terrades; Albert Berenguel; Debora Gil |
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A flexible outlier detector based on a topology given by graph communities |
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2020 |
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Arxiv |
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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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IAM; DAG; 600.139; 600.145; 600.140; 600.121 |
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Admin @ si @ RBG2020 |
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3475 |
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