no code implementations • 27 Sep 2022 • Andoni Elola, Elisabete Aramendi, Jorge Oliveira, Francesco Renna, Miguel T. Coimbra, Matthew A. Reyna, Reza Sameni, Gari D. Clifford, Ali Bahrami Rad
On the test set, the algorithm achieves an unweighted average of sensitivities of 80. 4% and an F1-score of 75. 8%.
1 code implementation • Computing in Cardiology 2022 • Matthew A. Reyna, Yashar Kiarashi, Andoni Elola, Jorge Oliveira, Francesco Renna, Annie Gu, Erick A. Perez Alday, Nadi Sadr, ASHISH SHARMA, Sandra Mattos, Miguel T. Coimbra, Reza Sameni, Ali Bahrami Rad, Gari D. Clifford
Objective Cardiac auscultation is an accessible diagnostic screening tool that can help to identify patients with heart murmurs for follow-up diagnostic screening and treatment, especially in resource-constrained environments.
no code implementations • 2 Aug 2021 • Jorge Oliveira, Francesco Renna, Paulo Dias Costa, Marcelo Nogueira, Cristina Oliveira, Carlos Ferreira, Alipio Jorge, Sandra Mattos, Thamine Hatem, Thiago Tavares, Andoni Elola, Ali Bahrami Rad, Reza Sameni, Gari D Clifford, Miguel T. Coimbra
This is mainly due to the lack of large publicly available datasets, where a more detailed description of such abnormal waves (e. g., cardiac murmurs) exists.
1 code implementation • 14 Feb 2019 • Vegard Antun, Francesco Renna, Clarice Poon, Ben Adcock, Anders C. Hansen
Deep learning, due to its unprecedented success in tasks such as image classification, has emerged as a new tool in image reconstruction with potential to change the field.
no code implementations • 11 Jul 2016 • Hugo Reboredo, Francesco Renna, Robert Calderbank, Miguel R. D. Rodrigues
This paper studies the classification of high-dimensional Gaussian signals from low-dimensional noisy, linear measurements.
no code implementations • 7 Aug 2015 • Jure Sokolic, Francesco Renna, Robert Calderbank, Miguel R. D. Rodrigues
This paper considers the classification of linear subspaces with mismatched classifiers.
no code implementations • 1 Dec 2014 • Francesco Renna, Liming Wang, Xin Yuan, Jianbo Yang, Galen Reeves, Robert Calderbank, Lawrence Carin, Miguel R. D. Rodrigues
These conditions, which are reminiscent of the well-known Slepian-Wolf and Wyner-Ziv conditions, are a function of the number of linear features extracted from the signal of interest, the number of linear features extracted from the side information signal, and the geometry of these signals and their interplay.