10 citations · 12 across the 5 of their papers we have counts for
5 papers
A metric learning approach for endoscopic kidney stone identification
Jorge Gonzalez-Zapata, Francisco Lopez-Tiro, Elias Villalvazo-Avila +5
Several Deep Learning (DL) methods have recently been proposed for an automated identification of kidney stones during an ureteroscopy to enable rapid therapeutic decisions. Even i…
Deep learning-based image exposure enhancement as a pre-processing for an accurate 3D colon surface reconstruction
Ricardo Espinosa, Carlos Axel Garcia-Vega, Gilberto Ochoa-Ruiz +2
This contribution shows how an appropriate image pre-processing can improve a deep-learning based 3D reconstruction of colon parts. The assumption is that, rather than global image…
Deep Prototypical-Parts Ease Morphological Kidney Stone Identification and are Competitively Robust to Photometric Perturbations
Daniel Flores-Araiza, Francisco Lopez-Tiro, Jonathan El-Beze +4
Identifying the type of kidney stones can allow urologists to determine their cause of formation, improving the prescription of appropriate treatments to diminish future relapses.…
A Novel Hybrid Endoscopic Dataset for Evaluating Machine Learning-based Photometric Image Enhancement Models
Axel Garcia-Vega, Ricardo Espinosa, Gilberto Ochoa-Ruiz +4
Endoscopy is the most widely used medical technique for cancer and polyp detection inside hollow organs. However, images acquired by an endoscope are frequently affected by illumin…
Construction of extended 3D field of views of the internal bladder wall surface: a proof of concept
Achraf Ben-Hamadou, Christian Daul, Charles Soussen
3D extended field of views (FOVs) of the internal bladder wall facilitate lesion diagnosis, patient follow-up and treatment traceability. In this paper, we propose a 3D image mosai…