no code implementations • 1 Feb 2024 • Ariadna Jiménez-Partinen, Miguel A. Molina-Cabello, Karl Thurnhofer-Hemsi, Esteban J. Palomo, Jorge Rodríguez-Capitán, Ana I. Molina-Ramos, Manuel Jiménez-Navarro
In addition, baseline classification methods are proposed and analyzed, validating the functionality of CADICA and giving the scientific community a starting point to improve CAD detection.
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.
no code implementations • 16 Jul 2020 • Karl Thurnhofer-Hemsi, Ezequiel López-Rubio, Miguel A. Molina-Cabello, Kayvan Najarian
Experimental results show that OKSVM performs better irrespective of the initial values of the RBF hyperparameter.
1 code implementation • 14 Jun 2020 • Saul Calderon-Ramirez, Luis Oala, Jordina Torrents-Barrena, Shengxiang Yang, Armaghan Moemeni, Wojciech Samek, Miguel A. Molina-Cabello
In this work, we propose MixMOOD - a systematic approach to mitigate effect of class distribution mismatch in semi-supervised deep learning (SSDL) with MixMatch.