no code implementations • 13 Jul 2023 • Jorge Gonzalez-Zapata, Francisco Lopez-Tiro, Elias Villalvazo-Avila, Daniel Flores-Araiza, Jacques Hubert, Andres Mendez-Vazquez, Gilberto Ochoa-Ruiz, Christian Daul
The proposed Guided Deep Metric Learning approach is based on a novel architecture which was designed to learn data representations in an improved way.
1 code implementation • 8 Apr 2023 • Daniel Flores-Araiza, Francisco Lopez-Tiro, Jonathan El-Beze, Jacques Hubert, Miguel Gonzalez-Mendoza, Gilberto Ochoa-Ruiz, Christian Daul
Using PPs in the classification task enables case-based reasoning explanations for such output, thus making the model interpretable.
no code implementations • 6 Apr 2023 • Francisco Lopez-Tiro, Elias Villalvazo-Avila, Juan Pablo Betancur-Rengifo, Ivan Reyes-Amezcua, Jacques Hubert, Gilberto Ochoa-Ruiz, Christian Daul
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 features for the identification of the type of kidney stones seen in endoscopic images.
no code implementations • 5 Nov 2022 • Elias Villalvazo-Avila, Francisco Lopez-Tiro, Jonathan El-Beze, Jacques Hubert, Miguel Gonzalez-Mendoza, Gilberto Ochoa-Ruiz, Christian Daul
Moreover, in comparison to the state-of-the-art, the fusion of the deep features improved the overall results up to 11% in terms of kidney stone classification accuracy.
no code implementations • 24 Oct 2022 • Francisco Lopez-Tiro, Juan Pablo Betancur-Rengifo, Arturo Ruiz-Sanchez, Ivan Reyes-Amezcua, Jonathan El-Beze, Jacques Hubert, Michel Daudon, Gilberto Ochoa-Ruiz, Christian Daul
Finally, in comparison to models that are trained from scratch or by initializing ImageNet weights, the obtained results suggest that the two-step approach extracts features improving the identification of kidney stones in endoscopic images.
no code implementations • 1 Jun 2022 • Daniel Flores-Araiza, Francisco Lopez-Tiro, Elias Villalvazo-Avila, Jonathan El-Beze, Jacques Hubert, Gilberto Ochoa-Ruiz, Christian Daul
Identifying the type of kidney stones can allow urologists to determine their formation cause, improving the early prescription of appropriate treatments to diminish future relapses.
no code implementations • 31 May 2022 • Elias Villalvazo-Avila, Francisco Lopez-Tiro, Daniel Flores-Araiza, Gilberto Ochoa-Ruiz, Jonathan El-Beze, Jacques Hubert, Christian Daul
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 features.
no code implementations • 2 May 2022 • Mauricio Mendez-Ruiz, Francisco Lopez-Tiro, Jonathan El-Beze, Vincent Estrade, Gilberto Ochoa-Ruiz1, Jacques Hubert, Andres Mendez-Vazquez, Christian Daul
Deep learning has shown great promise in diverse areas of computer vision, such as image classification, object detection and semantic segmentation, among many others.
no code implementations • 21 Jan 2022 • Francisco Lopez-Tiro, Vincent Estrade, Jacques Hubert, Daniel Flores-Araiza, Miguel Gonzalez-Mendoza, Gilberto Ochoa-Ruiz, Christian Daul
This pilot study compares the kidney stone recognition performances of six shallow machine learning methods and three deep-learning architectures which were tested with in-vivo images of the four most frequent urinary calculi types acquired with an endoscope during standard ureteroscopies.
no code implementations • 1 Mar 2021 • Francisco Lopez, Andres Varela, Oscar Hinojosa, Mauricio Mendez, Dinh-Hoan Trinh, Jonathan ElBeze, Jacques Hubert, Vincent Estrade, Miguel Gonzalez, Gilberto Ochoa, Christian Daul
Knowing the type (i. e., the biochemical composition) of kidney stones is crucial to prevent relapses with an appropriate treatment.