Publicaciones Científico Tecnológicas

Picon, Artzai; Medela, Alfonso; Sánchez-Peralta, Luisa F; Cicchi, Riccardo; Bilbao, Roberto; Alfieri, Domenico; Elola, Andoni; Glover, Ben; Saratxaga, Cristina L

Autofluorescence Image Reconstruction and Virtual Staining for In-Vivo Optical Biopsying (Artículo de revista)

IEEE Access, 2021.

(Enlaces | BibTeX | Etiquetas: convolutional neural networks, domain adaptation, histopathology analysis, optical biopsy, siamese semantic regression networks, virtual staining)

Sánchez-Peralta, Luisa F; Pagador, Blas J; Picón, Artzai; Calderón, Ángel José; Polo, Francisco; Andraka, Nagore; Bilbao, Roberto; Glover, Ben; Saratxaga, Cristina L; Sánchez-Margallo, Francisco M

PICCOLO White-Light and Narrow-Band Imaging Colonoscopic Dataset: A Performance Comparative of Models and Datasets (Artículo de revista)

Applied Sciences, 10 (23), pp. 8501, 2020.

(Enlaces | BibTeX | Etiquetas: )

Medela, Alfonso; Picón-Ruiz, Artzai

Constellation Loss: Improving the efficiency of deep metric learning loss functions for optimal embedding (Artículo de revista)

Journal of Pathology Informatics, 2020.

(Resumen | Enlaces | BibTeX | Etiquetas: deep learning;few shot;metric learning)

@article{Medela2020,
title = {Constellation Loss: Improving the efficiency of deep metric learning loss functions for optimal embedding},
author = {Alfonso Medela and Artzai Picón-Ruiz},
url = {https://www.jpathinformatics.org/article.asp?issn=2153-3539;year=2020;volume=11;issue=1;spage=38;epage=38;aulast=Medela
},
year = {2020},
date = {2020-11-26},
journal = {Journal of Pathology Informatics},
abstract = { Deep learning diagnostic algorithms are proving comparable results with human experts in a wide variety of tasks, and they still require a huge amount of well-annotated data for training, which is often non affordable. Metric learning techniques have allowed a reduction in the required annotated data allowing few-shot learning over deep learning architectures. Aims and Objectives: In this work, we analyze the state-of-the-art loss functions such as triplet loss, contrastive loss, and multi-class N-pair loss for the visual embedding extraction of hematoxylin and eosin (H&E) microscopy images and we propose a novel constellation loss function that takes advantage of the visual distances of the embeddings of the negative samples and thus, performing a regularization that increases the quality of the extracted embeddings. Materials and Methods: To this end, we employed the public H&E imaging dataset from the University Medical Center Mannheim (Germany) that contains tissue samples from low-grade and high-grade primary tumors of digitalized colorectal cancer tissue slides. These samples are divided into eight different textures (1. tumour epithelium, 2. simple stroma, 3. complex stroma, 4. immune cells, 5. debris and mucus, 6. mucosal glands, 7. adipose tissue and 8. background,). The dataset was divided randomly into train and test splits and the training split was used to train a classifier to distinguish among the different textures with just 20 training images. The process was repeated 10 times for each loss function. Performance was compared both for cluster compactness and for classification accuracy on separating the aforementioned textures. Results: Our results show that the proposed loss function outperforms the other methods by obtaining more compact clusters (Davis-Boulding: 1.41 ± 0.08, Silhouette: 0.37 ± 0.02) and better classification capabilities (accuracy: 85.0 ± 0.6) over H and E microscopy images. We demonstrate that the proposed constellation loss can be successfully used in the medical domain in situations of data scarcity.},
keywords = {deep learning;few shot;metric learning},
pubstate = {published},
tppubtype = {article}
}

Cerrar

Deep learning diagnostic algorithms are proving comparable results with human experts in a wide variety of tasks, and they still require a huge amount of well-annotated data for training, which is often non affordable. Metric learning techniques have allowed a reduction in the required annotated data allowing few-shot learning over deep learning architectures. Aims and Objectives: In this work, we analyze the state-of-the-art loss functions such as triplet loss, contrastive loss, and multi-class N-pair loss for the visual embedding extraction of hematoxylin and eosin (H&E) microscopy images and we propose a novel constellation loss function that takes advantage of the visual distances of the embeddings of the negative samples and thus, performing a regularization that increases the quality of the extracted embeddings. Materials and Methods: To this end, we employed the public H&E imaging dataset from the University Medical Center Mannheim (Germany) that contains tissue samples from low-grade and high-grade primary tumors of digitalized colorectal cancer tissue slides. These samples are divided into eight different textures (1. tumour epithelium, 2. simple stroma, 3. complex stroma, 4. immune cells, 5. debris and mucus, 6. mucosal glands, 7. adipose tissue and 8. background,). The dataset was divided randomly into train and test splits and the training split was used to train a classifier to distinguish among the different textures with just 20 training images. The process was repeated 10 times for each loss function. Performance was compared both for cluster compactness and for classification accuracy on separating the aforementioned textures. Results: Our results show that the proposed loss function outperforms the other methods by obtaining more compact clusters (Davis-Boulding: 1.41 ± 0.08, Silhouette: 0.37 ± 0.02) and better classification capabilities (accuracy: 85.0 ± 0.6) over H and E microscopy images. We demonstrate that the proposed constellation loss can be successfully used in the medical domain in situations of data scarcity.

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Sánchez-Peralta, Luisa F; Picón, Artzai; Sánchez-Margallo, Francisco M; Pagador, Blas J

Unravelling the effect of data augmentation transformations in polyp segmentation (Artículo de revista)

International journal of computer assisted radiology and surgery, 15 (12), pp. 1975–1988, 2020.

(Enlaces | BibTeX | Etiquetas: )

Galarza, Nekane; Rubio, Benjamin; Bereciartua-Pérez, Aranzazu; Lozano, Iván; Gascón, Jaime; Atxaga, Garbiñe; Perez, Jose

On the analysis of computer vision and ultrasound based techniques for the in-service inspection of aeronautics parts produced by Additive Layer Manufacturing (ALM) (Artículo de revista)

DYNA, 95 , pp. 371-375, 2020.

(Enlaces | BibTeX | Etiquetas: additive manufacturing, computer vision, defects detection, ultrasound)

Argüeso, David; Picon, Artzai; Irusta, Unai; Medela, Alfonso; San-Emeterio, Miguel G; Bereciartua, Arantza; Alvarez-Gila, Aitor

Few-Shot Learning approach for plant disease classification using images taken in the field (Artículo de revista)

Computers and Electronics in Agriculture, 175 , 2020.

(Enlaces | BibTeX | Etiquetas: bacterial plant disease, contrastive loss, convolutional neural networks, deep learning, few shot learning, fungal plant disease, plant disease, triplet loss)

Shahriari, M; Pardo, D; Picon, A; Del Ser, J; Torres-Verdín, C

A deep learning approach to the inversion of borehole resistivity measurements (Artículo de revista)

Computational Geosciences, 2020.

(Enlaces | BibTeX | Etiquetas: deep learning, deep neural networks, logging-while-drilling, real-time inversion, resistivity measurements, well geosteering)

Picon, Artzai; Alvarez-Gila, Aitor; Irusta, Unai; Echazarra, Jone

Why deep learning performs better than classical machine learning? (Artículo de revista)

DYNA, 95 , pp. 119-122, 2020.

(Enlaces | BibTeX | Etiquetas: deep learning)

Vicente, Asier; Picon, Artzai; Arteche, Jose Antonio; Linares, Miguel; Velasco, Arturo; Sainz, Jose Angel

Magnetic field-based arc stability sensor for electric arc furnaces (Artículo de revista)

Measurement, 151 , pp. 107134, 2020.

(Enlaces | BibTeX | Etiquetas: )

Picon, Artzai; Seitz, Maximiliam; Alvarez-Gila, Aitor; Mohnke, Patrick; Ortiz-Barredo, Amaia; Echazarra, Jone

Crop conditional Convolutional Neural Networks for massive multi-crop plant disease classification over cell phone acquired images taken on real field conditions (Artículo de revista)

Computers and Electronics in Agriculture, 2020.

(Enlaces | BibTeX | Etiquetas: )

Sánchez-Peralta, Luisa F; Picón, Artzai; Antequera-Barroso, Juan Antonio; Ortega-Morán, Juan Francisco; Sánchez-Margallo, Francisco M; Pagador, Blas J

Eigenloss: Combined PCA-Based Loss Function for Polyp Segmentation (Artículo de revista)

Mathematics, 8 (8), pp. 1316, 2020.

(Enlaces | BibTeX | Etiquetas: )

Sánchez-Peralta, Luisa F; Bote-Curiel, Luis; Picón, Artzai; Sánchez-Margallo, Francisco M; Pagador, Blas J

Deep learning to find colorectal polyps in colonoscopy: A systematic literature review (Artículo de revista)

Artificial Intelligence in Medicine, pp. 101923, 2020.

(Enlaces | BibTeX | Etiquetas: )

Barredo-Arrieta, Alejandro; Díaz-Rodríguez, Natalia; Ser, Javier Del; Bennetot, Adrien; Tabik, Siham; Barbado, Alberto; García, Salvador; Gil-López, Sergio; Molina, Daniel; Benjamins, Richard; Chatila, Raja; Herrera, Francisco

Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI (Artículo de revista)

arXiv preprint arXiv:1910.10045, 2019.

(Enlaces | BibTeX | Etiquetas: accountability, comprehensibility, data fusion, deep learning, explainability, Explainable Artificial Intelligence, fairness, interpretability, machine learning, privacy, responsible artificial intelligence, transparency)

Picón-Ruiz, Artzai; IRUSTA, UNAI; ÁLVAREZ-GILA, AITOR; ARAMENDI, ELISABETE; ALONSO-ATIENZA, FELIPE; FIGUERA, CARLOS; AYALA, UNAI; Garrote-Contreras, Estibaliz; WIK, LARS; KRAMER-JOHANSEN, JO; EFTESTØL, TRYGVE

Mixed convolutional and long short-term memory network for the detection of lethal ventricular arrhythmia (Artículo de revista)

PloS one, 14 (5), pp. e0216756, 2019.

(Resumen | Enlaces | BibTeX | Etiquetas: deep learning)

@article{picon2019plos,
title = {Mixed convolutional and long short-term memory network for the detection of lethal ventricular arrhythmia},
author = {Artzai Picón-Ruiz and UNAI IRUSTA and AITOR ÁLVAREZ-GILA and ELISABETE ARAMENDI and FELIPE ALONSO-ATIENZA and CARLOS FIGUERA and UNAI AYALA and Estibaliz Garrote-Contreras and LARS WIK and JO KRAMER-JOHANSEN and TRYGVE EFTESTØL},
url = {https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0216756},
year = {2019},
date = {2019-05-20},
journal = {PloS one},
volume = {14},
number = {5},
pages = {e0216756},
abstract = {Early defibrillation by an automated external defibrillator (AED) is key for the survival of out-of-hospital cardiac arrest (OHCA) patients. ECG feature extraction and machine learning have been successfully used to detect ventricular fibrillation (VF) in AED shock decision algorithms. Recently, deep learning architectures based on 1D Convolutional Neural Networks (CNN) have been proposed for this task. This study introduces a deep learning architecture based on 1D-CNN layers and a Long Short-Term Memory (LSTM) network for the detection of VF. Two datasets were used, one from public repositories of Holter recordings captured at the onset of the arrhythmia, and a second from OHCA patients obtained minutes after the onset of the arrest. Data was partitioned patient-wise into training (80%) to design the classifiers, and test (20%) to report the results. The proposed architecture was compared to 1D-CNN only deep learners, and to a classical approach based on VF-detection features and a support vector machine (SVM) classifier. The algorithms were evaluated in terms of balanced accuracy (BAC), the unweighted mean of the sensitivity (Se) and specificity (Sp). The BAC, Se, and Sp of the architecture for 4-s ECG segments was 99.3%, 99.7%, and 98.9% for the public data, and 98.0%, 99.2%, and 96.7% for OHCA data. The proposed architecture outperformed all other classifiers by at least 0.3-points in BAC in the public data, and by 2.2-points in the OHCA data. The architecture met the 95% Sp and 90% Se requirements of the American Heart Association in both datasets for segment lengths as short as 3-s. This is, to the best of our knowledge …},
keywords = {deep learning},
pubstate = {published},
tppubtype = {article}
}

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ELOLA, ANDONI; ARAMENDI, ELISABETE; IRUSTA, UNAI; Picón-Ruiz, Artzai; ALONSO, ERIK; OWENS, PAMELA; IDRIS, AHAMED

Deep Neural Networks for ECG-Based Pulse Detection during Out-of-Hospital Cardiac Arrest (Artículo de revista)

Entropy, 21 (3), pp. 305, 2019.

(Resumen | Enlaces | BibTeX | Etiquetas: biomedical engineering, deep learning, ecg)

@article{elola2019b,
title = {Deep Neural Networks for ECG-Based Pulse Detection during Out-of-Hospital Cardiac Arrest},
author = {ANDONI ELOLA and ELISABETE ARAMENDI and UNAI IRUSTA and Artzai Picón-Ruiz and ERIK ALONSO and PAMELA OWENS and AHAMED IDRIS},
url = {https://www.mdpi.com/1099-4300/21/3/305},
year = {2019},
date = {2019-03-01},
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. View Full-Text},
keywords = {biomedical engineering, deep learning, ecg},
pubstate = {published},
tppubtype = {article}
}

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SANCHEZ-PERALTA, LF; CALDERON, AJ; CABEZON, V; ORTEGA-MORAN, JF; SANCHEZ-MARGALLO, FM; POLO, F; SARATXAGA, CL; Picón-Ruiz, Artzai

SYSTEMATIC ACQUISITION AND ANNOTATION OF CLINICAL CASES FOR THE GENERATION OF A MEDICAL IMAGE DATABASE (Artículo en actas)

BRITISH JOURNAL OF SURGERY, pp. 16–16, WILEY 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 2019.

(BibTeX | Etiquetas: )

MEDELA, ALFONSO; Picón-Ruiz, Artzai; SARATXAGA, CRISTINA L; BELAR, OIHANA; CABEZON, VIRGINIA; CICCHI, RICCARDO; BILBAO, ROBERTO; BEN, GLOVER

Few shot learning in Histopathological images: Reducing the need of labeled data on biological datasets (Artículo en actas)

IEEE International Symposium on Biomedical Imaging, 2019.

(Enlaces | BibTeX | Etiquetas: deep learning, few shot learning)

Elola, Andoni; Aramendi, Elisabete; Irusta, Unai; Picón, Artzai; Alonso, Erik; Isasi, Iraia; Idris, Ahamed

Convolutional Recurrent Neural Networks to Characterize the Circulation Component in the Thoracic Impedance during Out-of-Hospital Cardiac Arrest (Artículo en actas)

2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 1921–1925, IEEE 2019.

(BibTeX | Etiquetas: )

Alvarez-Gila, Aitor; Galdran, Adrian; Garrote, Estibaliz; van de Weijer, Joost

Self-Supervised Blur Detection from Synthetically Blurred Scenes (Artículo de revista)

Image and Vision Computing, 92 , pp. 103804, 2019, ISSN: 0262-8856.

(Resumen | Enlaces | BibTeX | Etiquetas: Blur detection, deep learning, Defocus blur, Motion blur, Self-supervised learning, Synthetic)

BOTE-CURIEL, L; MORÁN, JF ORTEGA; PAGADOR, JB.; MARGALLO, FM. SÁNCHEZ; GLOVER, B; TEARE, J; POLO, F; ARBIDE, N; SARATXAGA, C L; SOLLEDER, P; ALFIERI, D; NOIA, DI F; ROYCROFT, B; BAIN, J; CICCHI, R; PAVONE, FS.; Picón-Ruiz, Artzai

Innovative multiphotonic endoscope to address technological challenges in current colonoscopy procedure (Artículo de revista)

CASEIB 2018, 2018.

(Enlaces | BibTeX | Etiquetas: deep learning)