1 code implementation • 26 May 2023 • Antonio J. Rivera, Miguel A. Dávila, David Elizondo, María J. del Jesus, Francisco Charte
Resampling algorithms are a useful approach to deal with imbalanced learning in multilabel scenarios.
no code implementations • 1 Mar 2022 • Saul Calderon-Ramirez, Shengxiang Yang, David Elizondo
In a semi-supervised setting, unlabelled data is used to improve the levels of accuracy and generalization of a model with small labelled datasets.
no code implementations • 17 Aug 2021 • Saul Calderon-Ramirez, Shengxiang Yang, David Elizondo, Armaghan Moemeni
This results in a distribution mismatch between the unlabelled and labelled datasets.
no code implementations • 24 Jul 2021 • Saul Calderon-Ramirez, Diego Murillo-Hernandez, Kevin Rojas-Salazar, David Elizondo, Shengxiang Yang, Miguel Molina-Cabello
The use of two popular and publicly available datasets (INbreast and CBIS-DDSM) as source data, to train and test the models on the novel target dataset, is evaluated.
1 code implementation • 10 Jun 2021 • Willard Zamora-Cardenas, Mauro Mendez, Saul Calderon-Ramirez, Martin Vargas, Gerardo Monge, Steve Quiros, David Elizondo, Miguel A. Molina-Cabello
To enforce the learning of morphological information per pixel, a deep distance transformer (DDT) acts as a back-bone model.
no code implementations • 19 Aug 2020 • Saul Calderon-Ramirez, Shengxiang-Yang, Armaghan Moemeni, David Elizondo, Simon Colreavy-Donnelly, Luis Fernando Chavarria-Estrada, Miguel A. Molina-Cabello
In this work we evaluate the performance of the semi-supervised deep learning architecture known as MixMatch using a very limited number of labelled observations and highly imbalanced labelled dataset.