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pro vyhledávání: '"Daul, A."'
Autor:
Reyes-Amezcua, Ivan, Rojas-Ruiz, Michael, Ochoa-Ruiz, Gilberto, Mendez-Vazquez, Andres, Daul, Christian
Deep learning developments have improved medical imaging diagnoses dramatically, increasing accuracy in several domains. Nonetheless, obstacles continue to exist because of the requirement for huge datasets and legal limitations on data exchange. A s
Externí odkaz:
http://arxiv.org/abs/2409.19934
Autor:
Reyes-Amezcua, Ivan, Espinosa, Ricardo, Daul, Christian, Ochoa-Ruiz, Gilberto, Mendez-Vazquez, Andres
Accurate depth estimation in endoscopy is vital for successfully implementing computer vision pipelines for various medical procedures and CAD tools. In this paper, we present the EndoDepth benchmark, an evaluation framework designed to assess the ro
Externí odkaz:
http://arxiv.org/abs/2409.19930
Evaluating the plausibility of synthetic images for improving automated endoscopic stone recognition
Autor:
Gonzalez-Perez, Ruben, Lopez-Tiro, Francisco, Reyes-Amezcua, Ivan, Falcon-Morales, Eduardo, Rodriguez-Gueant, Rosa-Maria, Hubert, Jacques, Daudon, Michel, Ochoa-Ruiz, Gilberto, Daul, Christian
Publikováno v:
2024 IEEE 37th International Symposium on Computer-Based Medical Systems (CBMS)
Currently, the Morpho-Constitutional Analysis (MCA) is the de facto approach for the etiological diagnosis of kidney stone formation, and it is an important step for establishing personalized treatment to avoid relapses. More recently, research has f
Externí odkaz:
http://arxiv.org/abs/2409.13409
Autor:
Flores-Araiza, Daniel, Lopez-Tiro, Francisco, Larose, Clément, Hinojosa, Salvador, Mendez-Vazquez, Andres, Gonzalez-Mendoza, Miguel, Ochoa-Ruiz, Gilberto, Daul, Christian
The in-vivo identification of the kidney stone types during an ureteroscopy would be a major medical advance in urology, as it could reduce the time of the tedious renal calculi extraction process, while diminishing infection risks. Furthermore, such
Externí odkaz:
http://arxiv.org/abs/2409.12883
Autor:
Villegas-Jimenez, Armando, Flores-Araiza, Daniel, Lopez-Tiro, Francisco, Daul, Gilberto Ochoa-Ruiz andand Christian
On the promise that if human users know the cause of an output, it would enable them to grasp the process responsible for the output, and hence provide understanding, many explainable methods have been proposed to indicate the cause for the output of
Externí odkaz:
http://arxiv.org/abs/2309.01921
Autor:
Gonzalez-Zapata, Jorge, Lopez-Tiro, Francisco, Villalvazo-Avila, Elias, Flores-Araiza, Daniel, Hubert, Jacques, Mendez-Vazquez, Andres, Ochoa-Ruiz, Gilberto, Daul, Christian
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 if these DL approaches led to promising results, they are mainly approp
Externí odkaz:
http://arxiv.org/abs/2307.07046
Autor:
Flores-Araiza, Daniel, Lopez-Tiro, Francisco, El-Beze, Jonathan, Hubert, Jacques, Gonzalez-Mendoza, Miguel, Ochoa-Ruiz, Gilberto, Daul, Christian
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. Currently, the associated ex-vivo diagnosis (known as Morpho-constitut
Externí odkaz:
http://arxiv.org/abs/2304.04077
Autor:
Lopez-Tiro, Francisco, Villalvazo-Avila, Elias, Betancur-Rengifo, Juan Pablo, Reyes-Amezcua, Ivan, Hubert, Jacques, Ochoa-Ruiz, Gilberto, Daul, Christian
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 en
Externí odkaz:
http://arxiv.org/abs/2304.03193
Autor:
Espinosa, Ricardo, Garcia-Vega, Carlos Axel, Ochoa-Ruiz, Gilberto, Lamarque, Dominique, Daul, Christian
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 illumination corrections, local under- and over-exposures should be c
Externí odkaz:
http://arxiv.org/abs/2304.03171
Autor:
Villalvazo-Avila, Elias, Lopez-Tiro, Francisco, El-Beze, Jonathan, Hubert, Jacques, Gonzalez-Mendoza, Miguel, Ochoa-Ruiz, Gilberto, Daul, Christian
This contribution presents a deep learning method for the extraction and fusion of information relating to kidney stone fragments acquired from different viewpoints of the endoscope. Surface and section fragment images are jointly used during the tra
Externí odkaz:
http://arxiv.org/abs/2211.02967