31 citations · 53 across the 6 of their papers we have counts for
6 papers
CADICA: a new dataset for coronary artery disease detection by using invasive coronary angiography
Ariadna Jiménez-Partinen, Miguel A. Molina-Cabello, Karl Thurnhofer-Hemsi +4
Coronary artery disease (CAD) remains the leading cause of death globally and invasive coronary angiography (ICA) is considered the gold standard of anatomical imaging evaluation w…
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…
Enforcing Morphological Information in Fully Convolutional Networks to Improve Cell Instance Segmentation in Fluorescence Microscopy Images
Willard Zamora-Cardenas, Mauro Mendez, Saul Calderon-Ramirez +5
Cell instance segmentation in fluorescence microscopy images is becoming essential for cancer dynamics and prognosis. Data extracted from cancer dynamics allows to understand and a…
Correcting Data Imbalance for Semi-Supervised Covid-19 Detection Using X-ray Chest Images
Saul Calderon-Ramirez, Shengxiang-Yang, Armaghan Moemeni +4
The Corona Virus (COVID-19) is an internationalpandemic that has quickly propagated throughout the world. The application of deep learning for image classification of chest X-ray i…
Radial basis function kernel optimization for Support Vector Machine classifiers
Karl Thurnhofer-Hemsi, Ezequiel López-Rubio, Miguel A. Molina-Cabello +1
Support Vector Machines (SVMs) are still one of the most popular and precise classifiers. The Radial Basis Function (RBF) kernel has been used in SVMs to separate among classes wit…
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…