|
Records |
Links |
|
Author |
Carles Sanchez; Miguel Viñas; Coen Antens; Agnes Borras; Debora Gil |
![download PDF file pdf](http://refbase.cvc.uab.es/img/file_PDF.gif)
![goto web page (via DOI) doi](http://refbase.cvc.uab.es/img/doi.gif)
|
|
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 |
|
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 ![sorted by Thesis field, descending order (down)](http://refbase.cvc.uab.es/img/sort_desc.gif) |
|
|
|
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 |
|
Permanent link to this record |
|
|
|
|
Author |
Debora Gil; Antoni Rosell |
![find record details (via OpenURL) openurl](http://refbase.cvc.uab.es/img/xref.gif)
|
|
Title |
Advances in Artificial Intelligence – How Lung Cancer CT Screening Will Progress? |
Type |
Abstract |
|
Year |
2019 |
Publication |
World Lung Cancer Conference |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
|
|
|
Keywords |
|
|
|
Abstract |
Invited speaker |
|
|
Address |
Barcelona; September 2019 |
|
|
Corporate Author |
|
Thesis ![sorted by Thesis field, descending order (down)](http://refbase.cvc.uab.es/img/sort_desc.gif) |
|
|
|
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 |
|
Permanent link to this record |
|
|
|
|
Author |
Debora Gil; Antonio Esteban Lansaque; Agnes Borras; Esmitt Ramirez; Carles Sanchez |
![download PDF file pdf](http://refbase.cvc.uab.es/img/file_PDF.gif)
![goto web page (via DOI) doi](http://refbase.cvc.uab.es/img/doi.gif)
|
|
Title |
Intraoperative Extraction of Airways Anatomy in VideoBronchoscopy |
Type |
Journal Article |
|
Year |
2020 |
Publication |
IEEE Access |
Abbreviated Journal |
ACCESS |
|
|
Volume |
8 |
Issue |
|
Pages |
159696 - 159704 |
|
|
Keywords |
|
|
|
Abstract |
A main bottleneck in bronchoscopic biopsy sampling is to efficiently reach the lesion navigating across bronchial levels. Any guidance system should be able to localize the scope position during the intervention with minimal costs and alteration of clinical protocols. With the final goal of an affordable image-based guidance, this work presents a novel strategy to extract and codify the anatomical structure of bronchi, as well as, the scope navigation path from videobronchoscopy. Experiments using interventional data show that our method accurately identifies the bronchial structure. Meanwhile, experiments using simulated data verify that the extracted navigation path matches the 3D route. |
|
|
Address |
|
|
|
Corporate Author |
|
Thesis ![sorted by Thesis field, descending order (down)](http://refbase.cvc.uab.es/img/sort_desc.gif) |
|
|
|
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 |
IAM; 600.139; 600.145 |
Approved |
no |
|
|
Call Number |
Admin @ si @ GEB2020 |
Serial |
3467 |
|
Permanent link to this record |
|
|
|
|
Author |
Debora Gil; Guillermo Torres |
![download PDF file pdf](http://refbase.cvc.uab.es/img/file_PDF.gif)
|
|
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 |
|
Issue |
|
Pages |
|
|
|
Keywords |
|
|
|
Abstract |
|
|
|
Address |
Virtual; June 2020 |
|
|
Corporate Author |
|
Thesis ![sorted by Thesis field, descending order (down)](http://refbase.cvc.uab.es/img/sort_desc.gif) |
|
|
|
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 |
CARS |
|
|
Notes |
IAM; 600.139; 600.145 |
Approved |
no |
|
|
Call Number |
Admin @ si @ GiT2020 |
Serial |
3472 |
|
Permanent link to this record |
|
|
|
|
Author |
Debora Gil; Oriol Ramos Terrades; Raquel Perez |
![download PDF file pdf](http://refbase.cvc.uab.es/img/file_PDF.gif)
|
|
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 |
|
Issue |
|
Pages |
|
|
|
Keywords |
|
|
|
Abstract |
|
|
|
Address |
Barcelona; September 2019 |
|
|
Corporate Author |
|
Thesis ![sorted by Thesis field, descending order (down)](http://refbase.cvc.uab.es/img/sort_desc.gif) |
|
|
|
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 |
IAM; DAG; 600.139; 600.145; 600.121 |
Approved |
no |
|
|
Call Number |
Admin @ si @ GRP2020 |
Serial |
3473 |
|
Permanent link to this record |
|
|
|
|
Author |
Debora Gil; Katerine Diaz; Carles Sanchez; Aura Hernandez-Sabate |
![download PDF file pdf](http://refbase.cvc.uab.es/img/file_PDF.gif)
![find record details (via OpenURL) openurl](http://refbase.cvc.uab.es/img/xref.gif)
|
|
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. |
|
|
Address |
|
|
|
Corporate Author |
|
Thesis ![sorted by Thesis field, descending order (down)](http://refbase.cvc.uab.es/img/sort_desc.gif) |
|
|
|
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 |
IAM; 600.139; 600.145; 601.337 |
Approved |
no |
|
|
Call Number |
Admin @ si @ GDS2020 |
Serial |
3474 |
|
Permanent link to this record |
|
|
|
|
Author |
Oriol Ramos Terrades; Albert Berenguel; Debora Gil |
![download PDF file pdf](http://refbase.cvc.uab.es/img/file_PDF.gif)
![find record details (via OpenURL) openurl](http://refbase.cvc.uab.es/img/xref.gif)
|
|
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. |
|
|
Address |
|
|
|
Corporate Author |
|
Thesis ![sorted by Thesis field, descending order (down)](http://refbase.cvc.uab.es/img/sort_desc.gif) |
|
|
|
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 |
IAM; DAG; 600.139; 600.145; 600.140; 600.121 |
Approved |
no |
|
|
Call Number |
Admin @ si @ RBG2020 |
Serial |
3475 |
|
Permanent link to this record |
|
|
|
|
Author |
Esmitt Ramirez; Carles Sanchez; Debora Gil |
![download PDF file pdf](http://refbase.cvc.uab.es/img/file_PDF.gif)
![goto web page (via DOI) doi](http://refbase.cvc.uab.es/img/doi.gif)
|
|
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 |
|
|
Corporate Author |
|
Thesis ![sorted by Thesis field, descending order (down)](http://refbase.cvc.uab.es/img/sort_desc.gif) |
|
|
|
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; 600.140; 601.337; 601.323 |
Approved |
no |
|
|
Call Number |
Admin @ si @ RSG2019 |
Serial |
3531 |
|
Permanent link to this record |
|
|
|
|
Author |
Guillermo Torres; Debora Gil |
![find record details (via OpenURL) openurl](http://refbase.cvc.uab.es/img/xref.gif)
|
|
Title |
A multi-shape loss function with adaptive class balancing for the segmentation of lung structures |
Type |
Journal Article |
|
Year |
2020 |
Publication |
International Journal of Computer Assisted Radiology and Surgery |
Abbreviated Journal |
IJCAR |
|
|
Volume |
15 |
Issue |
1 |
Pages |
S154-55 |
|
|
Keywords |
|
|
|
Abstract |
|
|
|
Address |
|
|
|
Corporate Author |
|
Thesis ![sorted by Thesis field, descending order (down)](http://refbase.cvc.uab.es/img/sort_desc.gif) |
|
|
|
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 |
IAM |
Approved |
no |
|
|
Call Number |
Admin @ si @ ToG2020 |
Serial |
3590 |
|
Permanent link to this record |
|
|
|
|
Author |
Sonia Baeza; R.Domingo; M.Salcedo; G.Moragas; J.Deportos; I.Garcia Olive; Carles Sanchez; Debora Gil; Antoni Rosell |
![goto web page url](http://refbase.cvc.uab.es/img/www.gif)
|
|
Title |
Artificial Intelligence to Optimize Pulmonary Embolism Diagnosis During Covid-19 Pandemic by Perfusion SPECT/CT, a Pilot Study |
Type |
Journal Article |
|
Year |
2021 |
Publication |
American Journal of Respiratory and Critical Care Medicine |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
|
|
|
Keywords |
|
|
|
Abstract |
|
|
|
Address |
|
|
|
Corporate Author |
|
Thesis ![sorted by Thesis field, descending order (down)](http://refbase.cvc.uab.es/img/sort_desc.gif) |
|
|
|
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 |
IAM; 600.145 |
Approved |
no |
|
|
Call Number |
Admin @ si @ BDS2021 |
Serial |
3591 |
|
Permanent link to this record |