Publications

@article{mazo_using_2020,

title = {Using an ontology of the human cardiovascular system to improve the classification of histological images},

author = {Claudia Mazo and Enrique Alegre and Maria Trujillo},

url = {https://www.nature.com/articles/s41598-020-69037-4},

year  = {2020},

date = {2020-01-01},

journal = {Scientific Reports},

volume = {10},

number = {1},

pages = {12276},

abstract = {This study improves histological image classification by using a Histological Ontology to correct misclassifications. The process involves analyzing small image regions with Local Binary Pattern (LBP) descriptors and Support Vector Machines (SVM) for initial classification. The ontology refines results by detecting impossible tissue configurations, enhancing accuracy. The method showed improved F-scores (0.769 to 0.886) and successfully recognized previously undetected epithelial tissue. A dataset of 6000 labeled histological tissue blocks is also provided for further research.},

note = {Publisher: Nature Publishing Group UK London},

keywords = {Histological Images, Human Cardiovascular System, Ontology},

pubstate = {published},

tppubtype = {article}

}

@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}

}