RT Conference Proceedings
T1 Feature density as an uncertainty estimator method in the binary classification mammography images task for a supervised deep learning model
A1 Hernández Vásquez, Marco A.
A1 Fuentes Fino, Ricardo Javier
A1 Calderón-Ramírez, Saúl
A1 Domínguez-Merino, Enrique
A1 López-Rubio, Ezequiel
A1 Molina-Cabello, Miguel Ángel
K1 Densidad
K1 Métodos estadísticos
K1 Computación hetereogénea
K1 Medidas de probabilidades
K1 Lógica
K1 Inteligencia artificial
K1 Bioinformática
K1 Ingeniería biomédica
AB Labeled medical datasets may include a limited number of observations for each class, while unlabeled datasets may include observations from patients with pathologies other than those observed in the labeled dataset. This negatively influences the performance of the prediction algorithms. Including out-of-distribution data in the unlabeled dataset can lead to varying degrees of performance degradation, or even improvement, by using a distance to measure how out-of-distribution a piece of data is. This work aims to propose an approach that allows estimating the predictive uncertainty of supervised algorithms, improving the behaviour when atypical samples are presented to the distribution of the dataset. In particular, we have used this approach to mammograms X-ray images applied to binary classification tasks. The proposal makes use of Feature Density, which consists of estimating the density of features from the calculation of a histogram. The obtained results report slight differences when different neural network architectures and uncertainty estimators are used
YR 2022
FD 2022
LK https://hdl.handle.net/10630/24482
UL https://hdl.handle.net/10630/24482
LA eng
NO Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 4 mar 2026