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Author Debora Gil; Antonio Esteban Lansaque; Sebastian Stefaniga; Mihail Gaianu; Carles Sanchez edit   pdf
url  openurl
  Title Data Augmentation from Sketch Type Conference Article
  Year 2019 Publication International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging Abbreviated Journal  
  Volume 11840 Issue Pages 155-162  
  Keywords Data augmentation; cycleGANs; Multi-objective optimization  
  Abstract State of the art machine learning methods need huge amounts of data with unambiguous annotations for their training. In the context of medical imaging this is, in general, a very difficult task due to limited access to clinical data, the time required for manual annotations and variability across experts. Simulated data could serve for data augmentation provided that its appearance was comparable to the actual appearance of intra-operative acquisitions. Generative Adversarial Networks (GANs) are a powerful tool for artistic style transfer, but lack a criteria for selecting epochs ensuring also preservation of intra-operative content.

We propose a multi-objective optimization strategy for a selection of cycleGAN epochs ensuring a mapping between virtual images and the intra-operative domain preserving anatomical content. Our approach has been applied to simulate intra-operative bronchoscopic videos and chest CT scans from virtual sketches generated using simple graphical primitives.
 
  Address Shenzhen; China; October 2019  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language (up) Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title LNCS  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference CLIP  
  Notes IAM; 600.145; 601.337; 600.139; 600.145 Approved no  
  Call Number Admin @ si @ GES2019 Serial 3359  
Permanent link to this record
 

 
Author Carles Sanchez; Miguel Viñas; Coen Antens; Agnes Borras; Debora Gil edit   pdf
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 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 (up) 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; Guillermo Torres edit   pdf
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 Issue Pages  
  Keywords  
  Abstract  
  Address Virtual; June 2020  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language (up) 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 edit   pdf
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 Issue Pages  
  Keywords  
  Abstract  
  Address Barcelona; September 2019  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language (up) 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 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  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language (up) 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 Jose Elias Yauri; Aura Hernandez-Sabate; Pau Folch; Debora Gil edit  doi
openurl 
  Title Mental Workload Detection Based on EEG Analysis Type Conference Article
  Year 2021 Publication Artificial Intelligent Research and Development. Proceedings 23rd International Conference of the Catalan Association for Artificial Intelligence. Abbreviated Journal  
  Volume 339 Issue Pages 268-277  
  Keywords Cognitive states; Mental workload; EEG analysis; Neural Networks.  
  Abstract The study of mental workload becomes essential for human work efficiency, health conditions and to avoid accidents, since workload compromises both performance and awareness. Although workload has been widely studied using several physiological measures, minimising the sensor network as much as possible remains both a challenge and a requirement.
Electroencephalogram (EEG) signals have shown a high correlation to specific cognitive and mental states like workload. However, there is not enough evidence in the literature to validate how well models generalize in case of new subjects performing tasks of a workload similar to the ones included during model’s training.
In this paper we propose a binary neural network to classify EEG features across different mental workloads. Two workloads, low and medium, are induced using two variants of the N-Back Test. The proposed model was validated in a dataset collected from 16 subjects and shown a high level of generalization capability: model reported an average recall of 81.81% in a leave-one-out subject evaluation.
 
  Address Virtual; October 20-22 2021  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language (up) Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference CCIA  
  Notes IAM; 600.139; 600.118; 600.145 Approved no  
  Call Number Admin @ si @ Serial 3723  
Permanent link to this record
 

 
Author Guillermo Torres; Jan Rodríguez Dueñas; Sonia Baeza; Antoni Rosell; Carles Sanchez; Debora Gil edit   pdf
url  openurl
  Title Prediction of Malignancy in Lung Cancer using several strategies for the fusion of Multi-Channel Pyradiomics Images Type Conference Article
  Year 2023 Publication 7th Workshop on Digital Image Processing for Medical and Automotive Industry in the framework of SYNASC 2023 Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract This study shows the generation process and the subsequent study of the representation space obtained by extracting GLCM texture features from computer-aided tomography (CT) scans of pulmonary nodules (PN). For this, data from 92 patients from the Germans Trias i Pujol University Hospital were used. The workflow focuses on feature extraction using Pyradiomics and the VGG16 Convolutional Neural Network (CNN). The aim of the study is to assess whether the data obtained have a positive impact on the diagnosis of lung cancer (LC). To design a machine learning (ML) model training method that allows generalization, we train SVM and neural network (NN) models, evaluating diagnosis performance using metrics defined at slice and nodule level.  
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  ISSN ISBN Medium  
  Area Expedition Conference DIPMAI  
  Notes IAM Approved no  
  Call Number Admin @ si @ TRB2023 Serial 3926  
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Author Guillermo Torres; Debora Gil; Antoni Rosell; S. Mena; Carles Sanchez edit  openurl
  Title Virtual Radiomics Biopsy for the Histological Diagnosis of Pulmonary Nodules Type Conference Article
  Year 2023 Publication 37th International Congress and Exhibition is organized by Computer Assisted Radiology and Surgery Abbreviated Journal  
  Volume Issue Pages  
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  Abstract Pòster  
  Address Munich; Germany; June 2023  
  Corporate Author Thesis  
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  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference CARS  
  Notes IAM Approved no  
  Call Number Admin @ si @ TGR2023a Serial 3950  
Permanent link to this record
 

 
Author Sonia Baeza; Debora Gil; Carles Sanchez; Guillermo Torres; Ignasi Garcia Olive; Ignasi Guasch; Samuel Garcia Reina; Felipe Andreo; Jose Luis Mate; Jose Luis Vercher; Antonio Rosell edit  openurl
  Title Biopsia virtual radiomica para el diagnóstico histológico de nódulos pulmonares – Resultados intermedios del proyecto Radiolung Type Conference Article
  Year 2023 Publication SEPAR Abbreviated Journal  
  Volume Issue Pages  
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  Address Granada; Spain; June 2023  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
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  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference SEPAR  
  Notes IAM Approved no  
  Call Number Admin @ si @ BGS2023 Serial 3951  
Permanent link to this record
 

 
Author Debora Gil; Guillermo Torres; Carles Sanchez edit  openurl
  Title Transforming radiomic features into radiological words Type Conference Article
  Year 2023 Publication IEEE International Symposium on Biomedical Imaging Abbreviated Journal  
  Volume Issue Pages  
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  Address Cartagena de Indias; Colombia; April 2023  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language (up) Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference ISBI  
  Notes IAM Approved no  
  Call Number Admin @ si @ GTS2023 Serial 3952  
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