RT Conference Proceedings
T1 Non-negative matrix factorization for medical imaging
A1 Stoean, Ruxandra
A1 Atencia-Ruiz, Miguel Alejandro
K1 Matemáticas aplicadas - Congresos
AB A non-negative matrix factorization approach to dimensionality reduction is proposed to aid classification of images. The original images can be stored as  lower-dimensional columns of a matrix that hold degrees of belonging to feature components, so they can be used in the training phase of the classification at lower runtime and without loss in accuracy. The extracted features can be visually examined and images reconstructed with limited error. The proof of concept is performed on a benchmark of handwritten digits, followed by the application to histopathological colorectal cancer slides. Results are encouraging, though dealing with real-world medical data raises a number of issues.
PB i6doc.com
YR 2018
FD 2018
LK https://hdl.handle.net/10630/15659
UL https://hdl.handle.net/10630/15659
LA eng
NO Atencia, Miguel, and Ruxandra Stoean. 2018. “Non-Negative Matrix Factorization for Medical Imaging.” In European Symposium on Artificial Neural Networks, edited by M. Verleysen, 379–384.
NO Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 21 ene 2026