activity
20202022
most citedMixMOOD: A systematic approach to class distribution mismatch in semi-supervised learning using deep dataset dissimilarity measures

6 citations · 9 across the 4 of their papers we have counts for

collaborators

4 papers

cs.CV20223 cited

Semi-supervised Deep Learning for Image Classification with Distribution Mismatch: A Survey

Saul Calderon-Ramirez, Shengxiang Yang, David Elizondo

Deep learning methodologies have been employed in several different fields, with an outstanding success in image recognition applications, such as material quality control, medical…

cs.CV2021

Dealing with Distribution Mismatch in Semi-supervised Deep Learning for Covid-19 Detection Using Chest X-ray Images: A Novel Approach Using Feature Densities

Saul Calderon-Ramirez, Shengxiang Yang, David Elizondo +1

In the context of the global coronavirus pandemic, different deep learning solutions for infected subject detection using chest X-ray images have been proposed. However, deep learn…

eess.IV2021

A Real Use Case of Semi-Supervised Learning for Mammogram Classification in a Local Clinic of Costa Rica

Saul Calderon-Ramirez, Diego Murillo-Hernandez, Kevin Rojas-Salazar +3

The implementation of deep learning based computer aided diagnosis systems for the classification of mammogram images can help in improving the accuracy, reliability, and cost of d…

cs.LG20206 cited

MixMOOD: A systematic approach to class distribution mismatch in semi-supervised learning using deep dataset dissimilarity measures

Saul Calderon-Ramirez, Luis Oala, Jordina Torrents-Barrena +4

In this work, we propose MixMOOD - a systematic approach to mitigate effect of class distribution mismatch in semi-supervised deep learning (SSDL) with MixMatch. This work is divid…