On the In Vivo Recognition of Kidney Stones Using Machine Learning

Author:

Lopez-Tiro Francisco1ORCID,Estrade Vincent2ORCID,Hubert Jacques3ORCID,Flores-Araiza Daniel1,Gonzalez-Mendoza Miguel1,Ochoa Gilberto1ORCID,Daul Christian4ORCID

Affiliation:

1. School of Engineering and Sciences, Tecnologico de Monterrey, Monterrey, Nuevo Leon, Mexico

2. CHU Pellegrin, Bordeaux, France

3. Service d’Urologie de Brabois, CHU Nancy, Vandoeuvre-lès-Nancy, France

4. Centre de Recherche en Automatique de Nancy, UMR 7039, CNRS, Université de Lorraine, Vandoeuvre-lès-Nancy, France

Funder

Azure Sponsorship Credits granted by Microsoft’s Artificial Intelligence (AI) for Good Research Laboratory through the AI for Health Program

French-Mexican Asociación Nacional de Universidades e Instituciones de Educación Superior (ANUIES) Consejo Nacional de de Humanidades, Ciencia y Tecnologia (CONAHCYT) Ecos Nord

Tecnologico de Monterrey through the ‘‘Challenge-Based Research Funding Program 2022’’

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Cited by 6 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. A metric learning approach for endoscopic kidney stone identification;Expert Systems with Applications;2024-12

2. A Review on Kidney Stone Detection using ML and DL Techniques;International Journal of Scientific Research in Computer Science, Engineering and Information Technology;2024-09-05

3. Kidney Disease Classification and Diagnosis: A Comprehensive Review of Current AI Techniques;2024 4th International Conference on Emerging Smart Technologies and Applications (eSmarTA);2024-08-06

4. Evaluating the plausibility of synthetic images for improving automated endoscopic stone recognition;2024 IEEE 37th International Symposium on Computer-Based Medical Systems (CBMS);2024-06-26

5. On the Link Between Model Performance and Causal Scoring of Medical Image Explanations;2024 IEEE 37th International Symposium on Computer-Based Medical Systems (CBMS);2024-06-26

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