Publications

@article{mazo_transfer_2018,

title = {Transfer learning for classification of cardiovascular tissues in histological images},

author = {Claudia Mazo and Jose Bernal and María Trujillo and Enrique Alegre},

url = {https://www.sciencedirect.com/science/article/pii/S0169260718305297},

year  = {2018},

date = {2018-01-01},

journal = {Computer methods and programs in biomedicine},

volume = {165},

pages = {69–76},

abstract = {This paper proposes an automatic method for classifying healthy tissues and organs from histology images using Convolutional Neural Networks (CNNs). The approach aims to address the challenges in automated tissue and organ recognition, which is crucial for educational and medical purposes. By leveraging the powerful capabilities of deep learning, particularly CNNs, the method seeks to improve classification accuracy and efficiency, building on prior advances in image processing and supervised learning.},

note = {Publisher: Elsevier},

keywords = {cardiovascular system, Fundamental Tissues, Histological Images, Organs, SVM, Transfer Learning},

pubstate = {published},

tppubtype = {article}

}

@article{mazo_histological_2017,

title = {A histological ontology of the human cardiovascular system},

author = {Claudia Mazo and Liliana Salazar and Oscar Corcho and Maria Trujillo and Enrique Alegre},

url = {https://link.springer.com/article/10.1186/s13326-017-0158-5},

year  = {2017},

date = {2017-01-01},

journal = {Journal of biomedical semantics},

volume = {8},

pages = {1–15},

abstract = {introduces a histological ontology of the human cardiovascular system, developed collaboratively by histology experts and computer scientists. The ontology follows a methodology based on Conceptual Models (CMs) and is validated using tools like OOPS!, expert evaluations, and its ability to answer Competency Questions (CQs). The ontology is publicly accessible at BioPortal and W3ID. 

The ontology is designed to support complex applications, including teaching, medical practices, bio-medical research, and natural language interactions.},

note = {Publisher: BioMed Central},

keywords = {bioinformatics, cardiovascular system, histological ontology, medical teaching and research},

pubstate = {published},

tppubtype = {article}

}