no code implementations • 22 Feb 2024 • Daniel Capellán-Martín, Abhijeet Parida, Juan J. Gómez-Valverde, Ramon Sanchez-Jacob, Pooneh Roshanitabrizi, Marius G. Linguraru, María J. Ledesma-Carbayo, Syed M. Anwar
We demonstrate improvements in TB detection performance ($\sim$12. 7% and $\sim$13. 4% top AUC/AUPR gains in adults and children, respectively) when conducting self-supervised pre-training when compared to fully-supervised (i. e., non pre-trained) ViT models, achieving top performances of 0. 959 AUC and 0. 962 AUPR in adult TB detection, and 0. 697 AUC and 0. 607 AUPR in zero-shot pediatric TB detection.
1 code implementation • 5 Sep 2023 • Daniel Capellán-Martín, Juan J. Gómez-Valverde, David Bermejo-Peláez, María J. Ledesma-Carbayo
In this work, we propose LightTBNet, a novel lightweight, fast and efficient deep convolutional network specially customized to detect TB from CXR images.
1 code implementation • 31 Jan 2023 • Daniel Capellán-Martín, Juan J. Gómez-Valverde, Ramon Sanchez-Jacob, David Bermejo-Peláez, Lara García-Delgado, Elisa López-Varela, Maria J. Ledesma-Carbayo
In clinical practice, experienced physicians assess TB by examining chest X-rays (CXR).