Publicaciones Científico Tecnológicas

@article{elola2019b,
title = {Deep Neural Networks for ECG-Based Pulse Detection during Out-of-Hospital Cardiac Arrest},
author = {Andoni Elola, Elisabete Aramendi, Unai Irusta, Artzai Picón, Erik Alonso, Pamela Owens, Ahamed Idris},
url = {https://www.mdpi.com/1099-4300/21/3/305},
year = {2019},
date = {2019-03-01},
journal = {Entropy},
abstract = {The automatic detection of pulse during out-of-hospital cardiac arrest (OHCA) is necessary for the early recognition of the arrest and the detection of return of spontaneous circulation (end of the arrest). The only signal available in every single defibrillator and valid for the detection of pulse is the electrocardiogram (ECG). In this study we propose two deep neural network (DNN) architectures to detect pulse using short ECG segments (5 s), ie, to classify the rhythm into pulseless electrical activity (PEA) or pulse-generating rhythm (PR). A total of 3914 5-s ECG segments, 2372 PR and 1542 PEA, were extracted from 279 OHCA episodes. Data were partitioned patient-wise into training (80%) and test (20%) sets. The first DNN architecture was a fully convolutional neural network, and the second architecture added a recurrent layer to learn temporal dependencies. Both DNN architectures were tuned using Bayesian optimization, and the results for the test set were compared to state-of-the art PR/PEA discrimination algorithms based on machine learning and hand crafted features. The PR/PEA classifiers were evaluated in terms of sensitivity (Se) for PR, specificity (Sp) for PEA, and the balanced accuracy (BAC), the average of Se and Sp. The Se/Sp/BAC of the DNN architectures were 94.1%/92.9%/93.5% for the first one, and 95.5%/91.6%/93.5% for the second one. Both architectures improved the performance of state of the art methods by more than 1.5 points in BAC. View Full-Text},
keywords = {deep learning},
pubstate = {published},
tppubtype = {article}
}

@inproceedings{medela2019b,
title = {Few shot learning in Histopathological images: Reducing the need of labeled data on biological datasets},
author = {Alfonso Medela and Artzai Picon and Cristina L. Saratxaga and Oihana Belar and Virginia Cabezon and Riccardo Cicchi and Roberto Bilbao and Glover Ben},
url = {https://computervision.tecnalia.com/wp-content/uploads/2019/06/ISBI_paper__final_version_-2.pdf},
year = {2019},
date = {2019-01-01},
booktitle = {IEEE International Symposium on Biomedical Imaging},
keywords = {deep learning, few shot learning},
pubstate = {published},
tppubtype = {inproceedings}
}

@article{bote2018,
title = {Innovative multiphotonic endoscope to address technological challenges in current colonoscopy procedure},
author = {L. Bote-Curiel and JF Ortega Morán and JB. Pagador and FM. Sánchez Margallo and B. Glover and J. Teare and F. Polo and N. Arbide and C.L. Saratxaga and P. Solleder and D. Alfieri and F. di Noia and B. Roycroft and J. Bain and R. Cicchi and FS. Pavone and A. Picon},
url = {https://computervision.tecnalia.com/wp-content/uploads/2019/06/CASEIB2018_PICCOLO_v9_conAutores-1.pdf},
year = {2018},
date = {2018-11-13},
journal = {CASEIB 2018},
keywords = {deep learning},
pubstate = {published},
tppubtype = {article}
}

@article{picon_deep_2018,
title = {Deep convolutional neural networks for mobile capture device-based crop disease classification in the wild},
author = {Artzai Picon and Aitor Alvarez-Gila and Maximiliam Seitz and Amaia Ortiz-Barredo and Jone Echazarra and Alexander Johannes},
url = {http://www.sciencedirect.com/science/article/pii/S0168169917312619
https://computervision.tecnalia.com/wp-content/uploads/2018/10/preprint_computer_and_electronics_10.1016@j.compag.2018.04.002-1.pdf},
doi = {10.1016/j.compag.2018.04.002},
issn = {0168-1699},
year = {2018},
date = {2018-01-01},
urldate = {2018-06-26},
journal = {Computers and Electronics in Agriculture},
abstract = {Fungal infection represents up to 50% of yield losses, making it necessary to apply effective and cost efficient fungicide treatments, whose efficacy depends on infestation type, situation and time. In these cases, a correct and early identification of the specific infection is mandatory to minimize yield losses and increase the efficacy and efficiency of the treatments. Over the last years, a number of image analysis-based methodologies have been proposed for automatic image disease identification. Among these methods, the use of Deep Convolutional Neural Networks (CNNs) has proven tremendously successful for different visual classification tasks. In this work we extend previous work by Johannes et al. (2017) with an adapted Deep Residual Neural Network-based algorithm to deal with the detection of multiple plant diseases in real acquisition conditions where different adaptions for early disease detection have been proposed. This work analyses the performance of early identification of three relevant European endemic wheat diseases: Septoria (Septoria triciti), Tan Spot (Drechslera triciti-repentis) and Rust (Puccinia striiformis & Puccinia recondita). The analysis was done using different mobile devices, and more than 8178 images were captured in two pilot sites in Spain and Germany during 2014,2015 and 2016. Obtained results reveal an overall improvement of the balanced accuracy from 0.78 (Johannes et al., 2017) up to 0.87 under exhaustive testing, and balanced accuracies greater than 0.96 on a pilot test performed in Germany.},
keywords = {cnn, convolutional neural networks, deep learning, disease identification, early pest, image processing, phytopathology, plant disease, precision agriculture},
pubstate = {published},
tppubtype = {article}
}

@inproceedings{picon_high_2017,
title = {High speed quality inspection of hot long metal products surface based on Deep Convolutional Neural Networks},
author = {Artzai Picon and Aitor {Alvarez-Gila} and Gorka Duro and Antonio {Cruz-Lopez} and Miguel Linares and Alberto Lago and Estibaliz Garrote and Jose Angel {Gutierrez Olabarria}},
url = {https://computervision.tecnalia.com/wp-content/uploads/2017/06/Surfin_DeepLearn_Bilbao2017.pdf},
year = {2017},
date = {2017-01-01},
booktitle = {Open session - International Summer School on Deep Learning 2017},
address = {Bilbao, Spain},
keywords = {deep learning, industry},
pubstate = {published},
tppubtype = {inproceedings}
}

@article{cruz-lopez_high-speed_2016,
title = {High-speed inspection system finds defects in steel},
author = {Antonio Cruz-Lopez and Alberto Lago and Roberto Gonzalez and Aitor Alvarez-Gila and Jose Angel Gutiérrez Olabarria},
url = {http://digital.vision-systems.com/visionsystems/201612?pg=26},
year = {2016},
date = {2016-01-01},
urldate = {2016-12-12},
journal = {Vision Systems Design},
number = {December 2016 - 1},
pages = {24--27},
keywords = {cnn, deep learning, neural networks, surface quality},
pubstate = {published},
tppubtype = {article}
}

@inproceedings{alvarez-gila_deep_2016b,
title = {Deep Convolutional Neural Networks for surface quality inspection of hot long metal products},
author = {Aitor Alvarez-Gila and Antonio Cruz-Lopez and Sergio Rodriguez-Vaamonde and Miguel Linares and José A Gutierrez-Olabarria and Estibaliz Garrote},
url = {https://computervision.tecnalia.com/wp-content/uploads/2016/09/EMVA-Deep-Convolutional-Neuronal-Networks-for-surface-quality-inspection-of-hot-long-metal-products.pdf},
year = {2016},
date = {2016-01-01},
booktitle = {First European Machine Vision Forum},
address = {Heidelberg, Germany},
keywords = {cnn, convolutional neural networks, deep learning, neural networks, surface quality},
pubstate = {published},
tppubtype = {inproceedings}
}

@article{elola2019a,
title = {Deep Neural Networks for ECG-Based Pulse Detection during Out-of-Hospital Cardiac Arrest},
author = {Andoni Elola, Elisabete Aramendi, Unai Irusta, Artzai Picón, Erik Alonso, Pamela Owens, Ahamed Idris},
url = {https://www.mdpi.com/1099-4300/21/3/305},
journal = {Entropy},
volume = {21},
number = {3},
pages = {305},
abstract = {The automatic detection of pulse during out-of-hospital cardiac arrest (OHCA) is necessary for the early recognition of the arrest and the detection of return of spontaneous circulation (end of the arrest). The only signal available in every single defibrillator and valid for the detection of pulse is the electrocardiogram (ECG). In this study we propose two deep neural network (DNN) architectures to detect pulse using short ECG segments (5 s), ie, to classify the rhythm into pulseless electrical activity (PEA) or pulse-generating rhythm (PR). A total of 3914 5-s ECG segments, 2372 PR and 1542 PEA, were extracted from 279 OHCA episodes. Data were partitioned patient-wise into training (80%) and test (20%) sets. The first DNN architecture was a fully convolutional neural network, and the second architecture added a recurrent layer to learn temporal dependencies. Both DNN architectures were tuned using Bayesian optimization, and the results for the test set were compared to state-of-the art PR/PEA discrimination algorithms based on machine learning and hand crafted features. The PR/PEA classifiers were evaluated in terms of sensitivity (Se) for PR, specificity (Sp) for PEA, and the balanced accuracy (BAC), the average of Se and Sp. The Se/Sp/BAC of the DNN architectures were 94.1%/92.9%/93.5% for the first one, and 95.5%/91.6%/93.5% for the second one. Both architectures improved the performance of state of the art methods by more than 1.5 points in BAC.},
keywords = {deep learning},
pubstate = {published},
tppubtype = {article}
}