Zobrazeno 1 - 10
of 320
pro vyhledávání: '"Ruiz, Gilberto A."'
While recent advances in deep learning (DL) for surgical scene segmentation have yielded promising results on single-center and single-imaging modality data, these methods usually do not generalize well to unseen distributions or modalities. Even tho
Externí odkaz:
http://arxiv.org/abs/2410.14821
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
Publikováno v:
2024 IEEE 37th International Symposium on Computer-Based Medical Systems (CBMS)
Frequent monitoring is necessary to stratify individuals based on their likelihood of developing gastrointestinal (GI) cancer precursors. In clinical practice, white-light imaging (WLI) and complementary modalities such as narrow-band imaging (NBI) a
Externí odkaz:
http://arxiv.org/abs/2409.12450
Autor:
Quihui-Rubio, Pablo Cesar, Flores-Araiza, Daniel, Gonzalez-Mendoza, Miguel, Mata, Christian, Ochoa-Ruiz, Gilberto
This contribution presents a deep learning method for the segmentation of prostate zones in MRI images based on U-Net using additive and feature pyramid attention modules, which can improve the workflow of prostate cancer detection and diagnosis. The
Externí odkaz:
http://arxiv.org/abs/2309.01322
Autor:
Guarduño-Martinez, Eduardo, Ciprian-Sanchez, Jorge, Valente, Gerardo, Vazquez-Garcia, Rodriguez-Hernandez, Gerardo, Palacios-Rosas, Adriana, Rossi-Tisson, Lucile, Ochoa-Ruiz, Gilberto
Wildfires represent one of the most relevant natural disasters worldwide, due to their impact on various societal and environmental levels. Thus, a significant amount of research has been carried out to investigate and apply computer vision technique
Externí odkaz:
http://arxiv.org/abs/2309.01318
Autor:
Quihui-Rubio, Pablo Cesar, Flores-Araiza, Daniel, Ochoa-Ruiz, Gilberto, Gonzalez-Mendoza, Miguel, Mata, Christian
This study focuses on comparing deep learning methods for the segmentation and quantification of uncertainty in prostate segmentation from MRI images. The aim is to improve the workflow of prostate cancer detection and diagnosis. Seven different U-Ne
Externí odkaz:
http://arxiv.org/abs/2308.04653
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