Publicacións

@inproceedings{botana_hybrid_2026,

title = {Hybrid Morphology-Based Tumor Detection from Breast MRI Segmentation Masks},

author = {Sergio Botana and Paula Puerta González and Pablo García Marcos and Sara Fernández Arias and Rebeca Oliveira Suárez and Guillermo Lorenzo and Héctor Gómez and Covadonga Camino and Víctor M. González and Angel Rio-Alvarez},

editor = {Aurelio López Fernández and Alejandro Rodríguez-González and Raquel Leirós-Rodríguez and Christian Mata Miquel and Víctor Manuel González Suárez},

doi = {10.1007/978-3-032-10661-2_26},

isbn = {978-3-032-10661-2},

year  = {2026},

date = {2026-01-01},

booktitle = {Artificial Intelligence in Biomedicine},

pages = {340–351},

publisher = {Springer Nature Switzerland},

address = {Cham},

abstract = {Magnetic resonance imaging (MRI) is widely used to diagnose breast cancer. However, the high sensitivity of dynamic contrast-enhanced MRI (DCE-MRI) comes at the cost of increased complexity in image interpretation and segmentation, which requires advanced techniques to help radiologists delineate tumor boundaries accurately and efficiently. This work proposes an automated methodology that reduces the reliance on manual annotations by integrating unsupervised clustering with supervised classification to identify the tumor cluster within masks derived from clustering techniques using only morphological characteristics, thus facilitating rapid, accurate, and reproducible tumor detection in breast magnetic resonance imaging.},

keywords = {Breast cancer, Morphology-based classification, Tumor classification},

pubstate = {published},

tppubtype = {inproceedings}

}