Kevin Ivan Barrera Llanga

Control, Data and Artificial Intelligence Group – CoDAlab

Technical University of Catalonia (UPC)

Eduard Maristany, 19

08930 Barcelona, Spain

e-mail: kevin.barrera@upc.edu

 

 

 

 

 

Current position

 

Postdoctoral Researcher (PostDoc) and Teaching and Research Staff (PDI) at the Universitat Politècnica de Catalunya (UPC).

 

Education

 

  • PhD in Automatics, Robotics, and Vision (Cum Laude), Universitat Politècnica de Catalunya (2024).
  • Master in Mechatronics Engineering (TFM Award), Universitat Politècnica de València, Colombia (2019).
  • Mechatronics Engineer, Universidad de las Fuerzas Armadas ESPE (2016).

 

Research interests

 

My research focuses on machine learning, deep learning, and its applications in the classification of abnormal lymphoid cells from peripheral blood digital images to support hematological diagnosis. This research is carried out through the cooperation between the Hospital Clinic of Barcelona and the Codalab group. Additionally, I am interested in applying neural networks and algorithms for classification in scenarios with limited data

 

Awards

 

  • Recognized as a member of the high citation research team 2023 of the Universitat Politècnica de Catalunya (UPC), for contributions to the article «Atypical lymphoid cells circulating in blood in COVID-19 infection: morphology, immunophenotype, and prognosis value,» in collaboration with UPC and Hospital Clínic de Barcelona (Barcelona – Spain).
  • Trainee Travel Award 2022: For the research «Automatic recognition of hypogranulation and cytoplasmic inclusions in neutrophils using sequential convolutional neural networks» (Bologna – Italy).
  • Berend Houwen Award 2021: For the research «Generation of artificial images of leukocytes by means of new generative adversarial networks (LeukocytesGAN)» presented by the International Society for Laboratory Hematology (Illinois – USA).
  • Berend Houwen Travel Award 2020: For the research «Automatic generation of artificial images of peripheral blood cells using generative artificial networks (GANs)» presented by the International Society for Laboratory Hematology (Melbourne – Australia).
  • Best Master’s Thesis Award in Mechatronics 2019: For the thesis «Prototype of an elevator and its control comparing different drive technologies» presented by the Mahle Chair (Valencia – Spain).
  • Collaboration scholarship for R&D activities 2018-2019 aimed at new research (Valencia – Spain).
  • Nagares Mechatronics Chair Scholarship 2017-2018 for Academic Excellence (Valencia – Spain).
  • Finalist in the «National Awards 2016» for the project «Automatic detection of pyroclastic flows in active volcanoes of Ecuador» organized by the Secretary of Higher Education, Science, Technology, and Innovation (Quito – Ecuador).
  • Second Place in the ESPE Scientific Innovation and Development contest for the project «Trajectory determination through semantic segmentation of pyroclastic flows in active volcanoes of Ecuador.»

 

 

 

Publications

 

Journal Articles

 

Barrera, K., Rodellar, J., Alférez, S., & Merino, A. (2024). A deep learning approach for automatic recognition of abnormalities in the cytoplasm of neutrophils. Computers in Biology and Medicine, 108691.

 

Barrera, K., Rodellar, J., Alférez, S., & Merino, A. (2023). Automatic normalized digital color staining in the recognition of abnormal blood cells using generative adversarial networks. Computer Methods and Programs in Biomedicine, 107629. Doi: 10.1016/j.cmpb.2023.107629

 

Barrera-Llanga, K., Burriel-Valencia, J., Sapena-Bañó, Á., & Martínez-Román, J. (2023). A Comparative Analysis of Deep Learning Convolutional Neural Network Architectures for Fault Diagnosis of Broken Rotor Bars in Induction Motors. Sensors, 23(19), 8196. Doi: 10.3390/s23198196

 

Barrera, K., Merino, A., Molina, A., & Rodellar, J. (2023). Automatic generation of artificial images of leukocytes and leukemic cells using generative adversarial networks (syntheticcellgan). Computer Methods and Programs in Biomedicine, 229, 107314. Doi: 10.1016/j.cmpb.2022.107314

 

Rodellar, J., Barrera, K., Alférez, S., Boldú, L., Laguna, J., Molina, A., & Merino, A. (2022). A Deep Learning Approach for the Morphological Recognition of Reactive Lymphocytes in Patients with COVID-19 Infection. Bioengineering, 9(5), 229. Doi: 10.3390/bioengineering9050229

 

Merino, A., Vlagea, A., Molina, A., Egri, N., Laguna, J., Barrera, K., … & Rodellar, J. (2022). Atypical lymphoid cells circulating in blood in COVID-19 infection: morphology, immunophenotype and prognosis value. Journal of Clinical Pathology, 75(2), 104-111. Doi: 10.1136/jclinpath-2020-207087

 

Barrera-Llanga, K., Sapena-Bañó, Á., Martínez-Román, J., & Puche-Panadero, R. (2023). Implementing Deep Learning Models in Embedded Systems for Diagnosis Induction Machine. International Journal of Electrical and Computer Engineering Research, 3(1), 7-12. Doi: 10.53375/ijecer.2023.319

 

Barrera, K., Merino, A., Rodriguez-Garcia, M., Laguna, J., Molina, A., & Rodellar, J. (2023, January). Automatic recognition of hypogranulation and cytoplasmic inclusions in neutrophils using sequential convolutional neural networks. International Journal of Laboratory Hematology, 45, 16-16. 111 RIVER ST, HOBOKEN 07030-5774, NJ USA: WILEY.

 

Cruz, C.,  Barrera, K., Viteri, F., Mendoza, D. (2018). Use of ICT’s to Generate Real-Time Alerts Based on the Automatic Analysis by the Artificial Vision System that Monitors Eruptive Processes. Journal of Engineering and Applied Sciences. doi: 10.36478/jeasci.2018.3169.3176

 

Viteri, F., Barrera, K., Cruz, C., Mendoza, D. (2018). Using Computer Vision Techniques to Generate Embedded Systems for Monitoring Volcanoes in Ecuador with Trajectory Determination. Journal of Engineering and Applied Sciences. doi: 10.36478/jeasci.2018.3164.3168

 

Barrera, K., Viteri, F., Cruz, C., Mendoza, D. (2018). Synergistic Integration of Techniques of VC, Communication Technologies and Unities of Calculation Transportable for Generate a System Embedded That Monitors Pyroclastic Flows in Real Time. Journal of Engineering and Applied Sciences. doi: 10.36478/jeasci.2018.3167.3171

 

Conference Proceedings

 

Hypogranulation and inclusions in neutrophils: deep learning models for the generation of artificial images and automatic recognition (EuroMedLab2023) (Roma-Italia).

 

Vision transformer for automatic image recognition of peripheral blood cells (ISLH2023) (New Orleans-USA).

 

Automatic recognition of hypogranulation and cytoplasmic inclusions in neutrophils using sequential convolutional neural networks (ISLH2022) (Bolonia-Italia).

 

Implementing deep learning models in embedded systems for diagnosis induction machine (ICMECE 2022) (Barcelona-España).

 

Generation of artificial images of leukocytes by means of new generative adversarial networks, LeukocytesGAN (ISLH2021) (Illinois-USA).

 

Generative Adversarial Networks for Peripheral Cell Blood Images Standardization (CASEIB XXXVIII) (Madrid-España).

 

Barrera, K. J., Merino, A., Acevedo, A., Boldú, L., & Molina, Á. (2021). Generation of Artificial Images of Leukocytes by means of New Generative Adversarial Networks. International Society for Laboratory Hematology, 38.

 

Merino, A., Laguna, J., Boldú, L., Molina, Á., & Barrera, K. J. (2021). Morphology and Automatic recognition of Atypical Lymphocytes in peripheral blood in COVID-19 patients. International Journal of Laboratory Hematology, 38.

 

Acevedo, A., Merino, A., Boldú, L., Molina, Á., & Barrera, K. J. (2021). Modular System for Neutrophil Recognition and Myelodysplastic Syndrome Diagnosis. International Journal of Laboratory Hematology, 38.

 

Automatic generation of artificial images of peripheral blood cells using generative artificial networks (GANs) (ISLH2020) (Melbourne-Australia).

 

Comparison Between Free Software (Python) and Proprietary Software (Matlab) for Digital Image Processing, Processing Speed Measurement and Peripheral Board Control, Applied to the Monitoring of Pyroclastic Flows in Eruptive Processes (JIISIC-CEIS’2017) (Quito- Ecuador).

 

Use of Neural Networks for the Detection of Pyroclastic Flows in Ecuador Volcanoes (JIISIC-CEIS 2017) (Quito- Ecuador).

 

 

Research Projects

 

Member of the research team in the competitive R&D project «Computational System for the Diagnosis of Acute Leukemia and Lymphoma from Peripheral Blood Images: Proof of Concept and Roadmap for Technological Valorization.» Funding entity code: PDC2022-133514-I00.

 

Member of the research team in the competitive R&D project «Computational hematopathology: deep learning solutions for the diagnosis of hematological diseases from peripheral blood cell images.» Funding entity code: PID2019-104087RB-I00.

 

Part of the research staff in the project «R+D+i project grant,» reference PID2021-128013OB-I00, sponsored by the MCIN/AEI/10.13039/501100011033 and conducted at the Energy Engineering Institute of the Universitat Politècnica de València.

 

Part of the research staff in the project «Proyectos I+D+i – Retos Investigación 2018,» reference RTI2018-102175-B-I00, sponsored by the MCIU/AEI/FEDER, EU, and conducted at the Energy Engineering Institute of the Universitat Politècnica de València.