4 citations · 16 across the 26 of their papers we have counts for
5 papers · 1 filter
Assessing the generalization performance of SAM for ureteroscopy scene understanding
Martin Villagrana, Francisco Lopez-Tiro, Clement Larose +2
The segmentation of kidney stones is regarded as a critical preliminary step to enable the identification of urinary stone types through machine- or deep-learning-based approaches.…
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…
Improving automatic endoscopic stone recognition using a multi-view fusion approach enhanced with two-step transfer learning
Francisco Lopez-Tiro, Elias Villalvazo-Avila, Juan Pablo Betancur-Rengifo +4
This contribution presents a deep-learning method for extracting and fusing image information acquired from different viewpoints, with the aim to produce more discriminant object f…
Multi-Scale Structural-aware Exposure Correction for Endoscopic Imaging
Axel Garcia-Vega, Ricardo Espinosa, Luis Ramirez-Guzman +5
Endoscopy is the most widely used imaging technique for the diagnosis of cancerous lesions in hollow organs. However, endoscopic images are often affected by illumination artefacts…
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…