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_classification_2017,

title = {Classification of cardiovascular tissues using LBP based descriptors and a cascade SVM},

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

url = {https://www.sciencedirect.com/science/article/pii/S0169260716305910},

year  = {2017},

date = {2017-01-01},

journal = {Computer methods and programs in biomedicine},

volume = {147},

pages = {1–10},

abstract = {Histological images have characteristics, such as texture, shape, colour and spatial structure, that permit the differentiation of each fundamental tissue and organ. Texture is one of the most discriminative features. The automatic classification of tissues and organs based on histology images is an open problem, due to the lack of automatic solutions when treating tissues without pathologies.},

note = {Publisher: Elsevier},

keywords = {Automatic Classification, Fundamental Tissues, Histology Images, image processing, Organs of the Cardiovascular System},

pubstate = {published},

tppubtype = {article}

}

@article{mazo_tissues_2017,

title = {Tissues classification of the cardiovascular system using texture descriptors},

author = {Claudia Mazo and Enrique Alegre and Maria Trujillo and Víctor González-Castro},

url = {https://link.springer.com/chapter/10.1007/978-3-319-60964-5_11},

year  = {2017},

date = {2017-01-01},

journal = {Annual Conference on Medical Image Understanding and Analysis},

pages = {123–132},

abstract = {This paper presents an automated classification approach for cardiovascular tissues using texture analysis. Rotation-invariant Local Binary Patterns (LBPri) and Haralick features were evaluated as descriptors, while Random Forest (RF) and Linear Discriminant Analysis (LDA) were tested for classification. The study categorized tissues into four classes, achieving high AUC values (up to 0.9994) using LBPri and RF, demonstrating the effectiveness of this method for tissue identification.},

note = {Publisher: Springer International Publishing Cham},

keywords = {Automatic Classification, Fundamental Tissues, Histology Images, image processing, Organs of the Cardiovascular System},

pubstate = {published},

tppubtype = {article}

}