Deep-Learning and HPC to Boost Biomedical Applications for Health

CORDIS fournit des liens vers les livrables publics et les publications des projets HORIZON.

Les liens vers les livrables et les publications des projets du 7e PC, ainsi que les liens vers certains types de résultats spécifiques tels que les jeux de données et les logiciels, sont récupérés dynamiquement sur OpenAIRE .

Livrables

ECVL library (II)

Documentation describing the deployed library from Tasks T3.1, T3.5. Draft in M17.

ECVL Toolkit front-end

Toolkit manual associated to Task T3.4.

EDDLL library (II)

Documentation describing deployed library from Tasks T2.1, T2.2 and T2.6. A draft in M17.

EDDLL Toolkit front-end

Toolkit manual associated to Task T2.5.

ECVL library

Documentation describing the deployed library from Tasks T3.1, T3.5. Draft in M17.

EDDLL library

Documentation describing deployed library from Tasks T2.1, T2.2 and T2.6. A draft in M17.

The Runtime system for DeepHealth libraries

This deliverable will release the HPC run-time with all functionalities and a report describing it. Intermediate delivery at M15.

The Runtime system for DeepHealth libraries (II)

This deliverable will release the HPC run-time with all functionalities and a report describing it. Intermediate delivery at M15.

Custom hardware for DeepHealth libraries

Report the activities of Task T55

Summer/Winter school and lessons learnt

This deliverable will summarize project lessons learnt and summerwinter school feedback

EDDLL Hardw. algorithms and adaptation to HPC (II)

Report describing the adaptation of heterogeneous components to the algorithms implemented on the library Deliverable associated to task T23 A draft in M17

ECVL Hw algorithms and adaptation to HPC infr.

Report describing the adaptation of heterogeneous components to the algorithms implemented on the library. Deliverable associated to task T3.2. Draft in M17.

Training toolkit

Will include all the specifications and requirements needed for Deep Learning algorithms training: frameworks, neural networks architecture & dataset (T1.4).

ECVL adaptation to cloud environments

Includes activities of Task 2.4.

Organisation of a thematic Hackathon

Will provide a hackathon simposium to promote the developed technology EDDLL and ECVL

Efficient HPC infrastr. for DeepHealth libraries (III)

This deliverable will report T51 and the advances of T52 and T53 Intermediate report at M15

EDDLL adaptation to Cloud

Includes activities of Task 2.4.

Final validation of DeepHealth concept

This deliverable will set the final validation process outcome of the whole DeepHealth concept Linked to task T610

Dissemination and comm. plans and report

This deliverable will provide an elaborate analysis of the stakeholder ecosystem and a plan of the targeted dissemination activities with a continuous reporting style. Tasks T7.1 and T7.2 involved.

Infrastructure & application adaptation requirements

Will include full detail of HPC infrastructure (T1.3) and optimizations for heterogeneous components and cloud (T1.8).

ECVL Hw algorithms and adaptation to HPC infr. (II)

Report describing the adaptation of heterogeneous components to the algorithms implemented on the library Deliverable associated to task T32 A draft in M17

EDDLL Hardw. algorithms and adaptation to HPC

Report describing the adaptation of heterogeneous components to the algorithms implemented on the library. Deliverable associated to task T2.3. A draft in M17.

Efficient HPC infrastr. for DeepHealth libraries (II)

This deliverable will report T51 and the advances of T52 and T53 Intermediate report at M15

API specifications for EDDLL and ECVL libraries

Will describe in full detail the API for the libraries to deploy, the deep-learning one (T1.5) and the computer vision one (T1.6).

Dissemination and comm. plans and report (II)

This deliverable will provide an elaborate analysis of the stakeholder ecosystem and a plan of the targeted dissemination activities with a continuous reporting style. Tasks T7.1 and T7.2 involved.

Validation of DeepHealth platforms and use cases

This deliverable will include report of the 7 targeted platforms and associated use cases Tasks T63 to T69

Dissemination and comm. plans and report (III)

This deliverable will provide an elaborate analysis of the stakeholder ecosystem and a plan of the targeted dissemination activities with a continuous reporting style Tasks T71 and T72 involved

Hybrid cloud computing solution

Report describing T56 task output

Efficient HPC infrastr. for DeepHealth libraries

This deliverable will report T5.1 and the advances of T5.2 and T5.3. Intermediate report at M15.

Validation of the DeepHealth libraries

This report will describe the validation process performed for the target libraries from tasks T61 and T62

ORDP: Open Research Data Pilot

This deliverable deals with the data collected and generated during the project, and central data, publications, etc.

Publications

Interpretable deep model for predicting gene-addicted non-small-cell lung cancer in CT scans

Auteurs: Pino, Carmelo; Palazzo, Simone; Trenta, Francesca; Cordero, Francesca; Bagci, Ulas; Rundo, Francesco; Battiato, Sebastiano; Giordano, Daniela; Aldinucci, Marco; Spampinato, Concetto
Publié dans: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), 2021, Page(s) 891-894, ISBN 978-1-6654-1246-9
Éditeur: IEEE
DOI: 10.1109/isbi48211.2021.9433832

AI Support for Accelerating Histopathological Slide Examinations of Prostate Cancer in Clinical Studies

Auteurs: Del Rio, Mauro; Lianas, Luca; Aspegren, Oskar; Busonera, Giovanni; Versaci, Francesco; Zelic, Renata; Vincent, Per H; Leo, Simone; Pettersson, Andreas; Akre, Olof; Pireddu, Luca;
Publié dans: 21st International Conference on IMAGE ANALYSIS AND PROCESSING. Lecture Notes in Computer Science, 2022, ISBN 978-3-031-13321-3
Éditeur: Springer, Cham
DOI: 10.1007/978-3-031-13321-3_48

Interactive-predictive neural multimodal systems

Auteurs: Peris, Álvaro; Casacuberta Nolla, Francisco
Publié dans: Pattern Recognition and Image Analysis. IbPRIA 2019. Lecture Notes in Computer Science(), vol 11867. Springer, Cham., 2019, Page(s) 16-28, ISBN 978-3-030-31332-6
Éditeur: Springer, Cham
DOI: 10.1007/978-3-030-31332-6_2

The DeepHealth Toolkit: A Unified Framework to Boost Biomedical Applications

Auteurs: Michele Cancilla; Laura Canalini; Federico Bolelli; Stefano Allegretti; Salvador Carrion; Roberto Paredes; Jon Ander Gómez; Simone Leo; Marco Enrico Piras; Luca Pireddu; Asaf Badouh; Santiago Marco-Sola; Lluc Alvarez; Miquel Moreto; Costantino Grana
Publié dans: International Conference on Pattern Recognition (ICPR) 2021, 2021, Page(s) 9881-9888, ISBN 978-1-7281-8808-9
Éditeur: IEEE
DOI: 10.1109/icpr48806.2021.9411954

Knowledge, Machine Learning and Atrial Fibrillation: More Ingredients for a Tastier Cocktail

Auteurs: Tomas Teijeiro
Publié dans: 2020 Computing in Cardiology Conference, 2020
Éditeur: IEEE
DOI: 10.22489/cinc.2020.476

Bringing AI pipelines onto cloud-HPC: setting a baseline for accuracy of COVID-19 diagnosis

Auteurs: Iacopo Colonnelli; Barbara Cantalupo; Concetto Spampinato; Matteo Pennisi; Marco Aldinucci
Publié dans: ENEA CRESCO in the fight against COVID-19, 2021, Page(s) 66-73, ISBN 978-88-8286-415-6
Éditeur: ENEA -TERIN-ICT-HPC
DOI: 10.5281/zenodo.5151510

WaveTF: A Fast 2D Wavelet Transform for Machine Learning in Keras

Auteurs: Versaci, Francesco
Publié dans: Pattern Recognition. ICPR International Workshops and Challenges. ICPR 2021. Lecture Notes in Computer Science, 2021, ISBN 978-3-030-68763-2
Éditeur: Springer, Cham
DOI: 10.1007/978-3-030-68763-2_46

Detection of Pulmonary Conditions Using the DeepHealth Framework

Auteurs: Carrión, Salvador; López-Chilet, Álvaro; Martı́nez-Bernia, Javier; Coll-Alonso, Joan; Chorro-Juan, Daniel; Gómez, Jon Ander
Publié dans: Image Analysis and Processing. ICIAP 2022 Workshops. ICIAP 2022. Lecture Notes in Computer Science, vol 13373. Springer, Cham., Numéro 6, 2022, Page(s) 557–566
Éditeur: Springer, Cham
DOI: 10.1007/978-3-031-13321-3_49

HPC Application Cloudification: The StreamFlow Toolkit

Auteurs: Colonnelli, Iacopo; Cantalupo, Barbara ; Esposito, Roberto ; Pennisi, Matteo ; Spampinato, Concetto ; Aldinucci, Marco
Publié dans: 12th Workshop on Parallel Programming and Run-Time Management Techniques for Many-core Architectures and 10th Workshop on Design Tools and Architectures for Multicore Embedded Computing Platforms (PARMA-DITAM 2021), 2021, Page(s) 5:1--5:13, ISBN 978-3-95977-181-8
Éditeur: Schloss Dagstuhl -- Leibniz-Zentrum für Informatik
DOI: 10.4230/oasics.parma-ditam.2021.5

Noise-Resilient and Interpretable Epileptic Seizure Detection

Auteurs: Anthony Hitchcock Thomas; Amir Aminifar; David Atienza
Publié dans: 2020 IEEE International Symposium on Circuits and Systems (ISCAS), 2020
Éditeur: IEEE
DOI: 10.1109/iscas45731.2020.9180429

Deep Learning e calcolo ad alte prestazioni per l'elaborazione di immagini biomediche

Auteurs: , Aldinucci; Berzovini; , Grana; , Grangetto; , Pireddu; , Zanetti
Publié dans: Convegno Nazionale Italiano sull'Intelligenza Artificiale (Ital-IA), 2019
Éditeur: CINI
DOI: 10.5281/zenodo.3338256

Deep-Learning and HPC to Boost Biomedical Applications for Health (DeepHealth)

Auteurs: Monica Caballero, Jon Ander Gomez, Aimilia Bantouna
Publié dans: 2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS), 2019, Page(s) 150-155, ISBN 978-1-7281-2286-1
Éditeur: IEEE
DOI: 10.1109/CBMS.2019.00040

An Event-Based System for Low-Power ECG QRS Complex Detection

Auteurs: Silvio Zanoli, Tomas Teijeiro, Fabio Montagna, David Atienza
Publié dans: 2020 Design, Automation & Test in Europe Conference & Exhibition (DATE), 2020, Page(s) 258-263, ISBN 978-3-9819263-4-7
Éditeur: IEEE
DOI: 10.23919/DATE48585.2020.9116498

Noise-Resilient and Interpretable Epileptic Seizure Detection

Auteurs: Hitchcock Thomas, Anthony; Aminifar, Amir; Atienza, David
Publié dans: 2020 IEEE International Symposium on Circuits and Systems (ISCAS)., Numéro 6, 2020
Éditeur: IEEE
DOI: 10.5281/zenodo.3903314

Unitopatho, A Labeled Histopathological Dataset for Colorectal Polyps Classification and Adenoma Dysplasia Grading

Auteurs: Carlo Alberto Barbano; Daniele Perlo; Enzo Tartaglione; Attilio Fiandrotti; Luca Bertero; Paola Cassoni; Marco Grangetto
Publié dans: IEEE International Conference on Image Processing (ICIP), Numéro 45, 2021
Éditeur: IEEE
DOI: 10.1109/icip42928.2021.9506198

Automatic Detection of Epileptic Seizures with Recurrent and Convolutional Neural Networks

Auteurs: Salvador Carrión; Álvaro López-Chilet; Javier Martı́nez-Bernia; Joan Coll-Alonso; Daniel Chorro-Juan; Jon Ander Gómez
Publié dans: Image Analysis and Processing. ICIAP 2022 Workshops. ICIAP 2022. Lecture Notes in Computer Science, vol 13373. Springer, Cham, 2022, Page(s) 522–532, ISBN 978-3-031-13321-3
Éditeur: Springer, Cham
DOI: 10.1007/978-3-031-13321-3_46

Scaling deep learning data management with Cassandra DB

Auteurs: Versaci, Francesco; Busonera, Giovanni
Publié dans: 2021 IEEE International Conference on Big Data (Big Data), 2021, ISBN 978-1-6654-3902-2
Éditeur: IEEE
DOI: 10.1109/bigdata52589.2021.9672005

A Two-Step Radiologist-Like Approach for Covid-19 Computer-Aided Diagnosis from Chest X-Ray Images

Auteurs: Carlo Alberto Barbano; Enzo Tartaglione; Claudio Berzovini; Marco Calandri; Marco Grangetto
Publié dans: Image Analysis and Processing – ICIAP 2022 ISBN: 9783031064265, Numéro 33, 2022
Éditeur: Springer Science
DOI: 10.1007/978-3-031-06427-2_15

Prognostic Utility of the Gleason Grading System Revisions and Histopathological Factors Beyond Gleason Grade

Auteurs: Renata Zelic; Francesca Giunchi; Jonna Fridfeldt; Jessica Carlsson; Sabina Davidsson; Luca Lianas; Cecilia Mascia; Daniela Zugna; Luca Molinaro; Per Henrik Vincent; Gianluigi Zanetti; Ove AndrÃ©n; Lorenzo Richiardi; Olof Akre; Michelangelo Fiorentino; Andreas Pettersson
Publié dans: Clinical Epidemiology, Numéro 18, 2022, Page(s) 59--70, ISSN 1179-1349
Éditeur: Dove Medical Press Ltd
DOI: 10.2147/clep.s339140

Distributed workflows with Jupyter

Auteurs: I.Colonnelli, M. Aldinucci, B. Cantalupo, L. Padovani, S. Rabellino, C. Spampinato, R. Morelli, R. Di Carlo, N. Magini, C. Cavazzoni
Publié dans: Future Generation Computer Systems, Numéro 0167739X, 2022, Page(s) 282-298, ISSN 0167-739X
Éditeur: Elsevier BV
DOI: 10.1016/j.future.2021.10.007

StreamFlow: cross-breeding cloud with HPC

Auteurs: Iacopo Colonnelli; Barbara Cantalupo; Ivan Merelli; Marco Aldinucci
Publié dans: IEEE Transactions on Emerging Topics in Computing, Numéro 9, 4, 2021, Page(s) 1723–1737, ISSN 2168-6750
Éditeur: IEEE Computer Society

The COUGHVID crowdsourcing dataset, a corpus for the study of large-scale cough analysis algorithms.

Auteurs: Lara Orlandic; Tomas Teijeiro; David Atienza
Publié dans: Scientific Data, Vol 8, Iss 1, Pp 1-10 (2021), Numéro 27, 2021, ISSN 2052-4463
Éditeur: Springer Nature
DOI: 10.48550/arxiv.2009.11644

De-identifying Spanish medical texts - Named Entity Recognition applied to radiology reports

Auteurs: Irene Pérez-Díez; Raúl Pérez-Moraga; Adolfo López-Cerdán; Marisa Caparrós Redondo; Jose-Maria Salinas-Serrano; María de la Iglesia-Vayá
Publié dans: Journal of Biomedical Semantics, Numéro 20411480, 2021, ISSN 2041-1480
Éditeur: Journal of Biomedical Semantics
DOI: 10.1186/s13326-021-00236-2

Unveiling COVID-19 from Chest X-ray with deeplearning: a hurdles race with small data

Auteurs: Tartaglione, Enzo; Barbano, Carlo Alberto; Berzovini, Claudio; Calandri, Marco; Grangetto, Marco
Publié dans: INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH, Numéro 17, 2020, ISSN 1660-4601
Éditeur: MDPI
DOI: 10.3390/ijerph17186933

Personalized Real-Time Federated Learning for Epileptic Seizure Detection

Auteurs: Saleh Baghersalimi; Tomas Teijeiro; David Atienza; Amir Aminifar
Publié dans: IEEE Journal of Biomedical and Health Informatics, 2022, ISSN 2168-2194
Éditeur: Institute of Electrical and Electronics Engineers Inc.
DOI: 10.1109/jbhi.2021.3096127

Modular Design and Optimization of Biomedical Applications for Ultralow Power Heterogeneous Platforms

Auteurs: Elisabetta de Giovanni; Fabio Montagna; Benoit Denkinger; Simone Machetti; Miguel Peón-Quirós; Simone Benatti; Davide Rossi; Luca Benini; David Atienza
Publié dans: IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, Numéro 4, 2020, ISSN 0278-0070
Éditeur: Institute of Electrical and Electronics Engineers
DOI: 10.1109/tcad.2020.3012652

Machine learning-based prediction of adverse events following an acute coronary syndrome (PRAISE): a modelling study of pooled datasets

Auteurs: Fabrizio D'Ascenzo; Ovidio De Filippo; Guglielmo Gallone; Gianluca Mittone; Marco Agostino Deriu; Mario Iannaccone; Albert Ariza-Solé; Christoph Liebetrau; Sergio Manzano-Fernández; Giorgio Quadri; Tim Kinnaird; Gianluca Campo; José P.S. Henriques; James M. Hughes; Alberto Dominguez-Rodriguez; Marco Aldinucci; Umberto Morbiducci; Giuseppe Patti; Sergio Raposeiras-Roubín; Emad Abu-Assi; Gaetano
Publié dans: The Lancet, Numéro 01406736, 2021, Page(s) 199-207, ISSN 0140-6736
Éditeur: The Lancet Publishing Group
DOI: 10.1016/s0140-6736(20)32519-8

Additional file 1 of Advantages of using graph databases to explore chromatin conformation capture experiments

Auteurs: D’Agostino, Daniele; Liò, Pietro; Aldinucci, Marco; Merelli, Ivan
Publié dans: BMC Bioinformatics, Numéro 14712105, 2021, ISSN 1471-2105
Éditeur: BioMed Central
DOI: 10.6084/m9.figshare.14490371

Advantages of using graph databases to explore chromatin conformation capture experiments.

Auteurs: Daniele D'Agostino; Pietro Liò; Marco Aldinucci; Ivan Merelli
Publié dans: BMC Bioinformatics, Numéro 14712105, 2021, ISSN 1471-2105
Éditeur: BioMed Central
DOI: 10.1186/s12859-020-03937-0

The CLAIRE COVID-19 initiative: approach, experiences and recommendations

Auteurs: G. Bontempi, R. Chavarriaga, H. De Canck, E. Girardi, H. Hoos, I. Kilbane-Dawe, T. Ball, A. Nowé, J. Sousa, D. Bacciu, M. Aldinucci, M. De Domenico, A. Saffiotti & M. Maratea
Publié dans: Ethics and Information Technology, Numéro 13881957, 2021, Page(s) 127–133, ISSN 1388-1957
Éditeur: Kluwer Academic Publishers
DOI: 10.1007/s10676-020-09567-7

An explainable AI system for automated COVID-19 assessment and lesion categorization from CT-scans

Auteurs: Pennisi, Matteo; Kavasidis, Isaak; Spampinato, Concetto; Schinina, Vincenzo; Palazzo, Simone; Proietto Salanitri, Federica; Bellitto, Giovanni; Rundo, Francesco; Aldinucci, Marco; Cristofaro, Massimo; Campioni, Paolo; Pianura, Elisa; Di Stefano, Federica; Petrone, Ada; Albarello, Fabrizio; Ippolito, Giuseppe; Cuzzocrea, Salvatore; Conoci, Sabrina
Publié dans: Artificial Intelligence in Medicine: 118, Numéro 09333657, 2021, ISSN 0933-3657
Éditeur: Elsevier BV
DOI: 10.1016/j.artmed.2021.102114

Adaptive R-Peak Detection on Wearable ECG Sensors for High-Intensity Exercise

Auteurs: De Giovanni, Elisabetta; Teijeiro, Tomas; P. Millet, Gregoire; Atienza, David
Publié dans: IEEE Transactions on Biomedical Engineering, Numéro 28, 2022, ISSN 0018-9294
Éditeur: Institute of Electrical and Electronics Engineers
DOI: 10.1109/TBME.2022.3205304

Resource-Aware Distributed Epilepsy Monitoring Using Self-Awareness From Edge to Cloud

Auteurs: Farnaz Forooghifar, Amir Aminifar, David Atienza
Publié dans: IEEE Transactions on Biomedical Circuits and Systems, Numéro 13/6, 2019, Page(s) 1338-1350, ISSN 1932-4545
Éditeur: Institute of Electrical and Electronics Engineers
DOI: 10.1109/tbcas.2019.2951222

Interpreting deep learning models for epileptic seizure detection on EEG signals.

Auteurs: Valentin Gabeff; Tomas Teijeiro; Marina Zapater; Marina Zapater; Leila Cammoun; Sylvain Rheims; Sylvain Rheims; Philippe Ryvlin; David Atienza
Publié dans: Artificial Intelligence in Medicine, Numéro 26, 2021, ISSN 0933-3657
Éditeur: Elsevier BV
DOI: 10.1016/j.artmed.2021.102084

MAGNETIC: Multi-Agent Machine Learning-Based Approach for Energy Efficient Dynamic Consolidation in Data Centers

Auteurs: Kawsar Haghshenas, Ali Pahlevan, Marina Zapater, Siamak Mohammadi, David Atienza
Publié dans: IEEE Transactions on Services Computing, 2019, Page(s) 1-1, ISSN 1939-1374
Éditeur: Institute of Electrical and Electronics Engineers
DOI: 10.1109/tsc.2019.2919555

DeepHealth perspective on HPC, Big Data, IoT and AI future industry-driven collaborative strategic topics

Auteurs: Caballero, Monica; Gomez, Jon A.
Publié dans: 2021
Éditeur: EuroHPC Joint Undertaking
DOI: 10.5281/zenodo.4670289

Diagnosticul digital urologic-o poveste de succes romaneasca

Auteurs: Dana Oniga, Robert Dobran, Elisa Ionascu
Publié dans: Stiinta si Tehnica, 2021, Page(s) 26-27
Éditeur: Stiinta & Tehnica

The DeepHealth Toolkit: A Key European Free and Open-Source Software for Deep Learning and Computer Vision Ready to Exploit Heterogeneous HPC and Cloud Architectures

Auteurs: Marco Aldinucci; David Atienza; Federico Bolelli; Mónica Caballero; Iacopo Colonnelli; José Flich; Jon A. Gómez; David González; Costantino Grana; Marco Grangetto; Simone Leo; Pedro López; Dana Oniga; Roberto Paredes; Luca Pireddu; Eduardo Quiñones; Tatiana Silva; Enzo Tartaglione; Marina Zapater
Publié dans: Technologies and Applications for Big Data Value . Springer, Cham. https://doi.org/10.1007/978-3-030-78307-5_9, 2022, Page(s) 183–202, ISBN 978-3-030-78306-8
Éditeur: Springer, Cham
DOI: 10.1007/978-3-030-78307-5_9

Chapter 10. The DeepHealth HPC Infrastructure: Leveraging Heterogenous HPC and Cloud Computing Infrastructures for IA-based Medical SolutionsChapter 11: Applications of AI and HPC in the Health Domain

Auteurs: E. Quiñones, J. Perales, J. Ejarque, A. Badouh, S. Marco, F. Auzanneau, F. Galea, D. González, J.R. Hervás, T. Silva, I. Colonnelli, B. Cantalupo, M. Aldinucci, E. Tartaglione, R. Tornero, J. Flich, J. M. Martínez, D. Rodriguez, I. Catalán, J. García, and C. Hernández (Chapter 10) D. O.niga, B. Cantalupo, D. Perlo, M. Grangetto, F. Bolelli, F. Pollastri, M. Cancilla, L. Canalini, C. Grana,
Publié dans: HPC, Big Data, and AI Convergence Towards Exascale, 2022, Page(s) 217-240, ISBN 9781032009841
Éditeur: London: CRC Press - Taylor & Francis Group, 2021
DOI: 10.1201/9781003176664

Análisis del funcionamiento cardíaco mediante redes neuronales

Auteurs: López Chilet, Álvaro
Publié dans: Análisis del funcionamiento cardíaco mediante redes neuronales, 2020
Éditeur: Universitat Politècnica de València (UPV)

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