Evaluating the plausibility of synthetic images for improving automated endoscopic stone recognition-Reference-Cited by-同舟云学术

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Evaluating the plausibility of synthetic images for improving automated endoscopic stone recognition

Published:2024-06-26 Issue: Volume:33 Page:126-133
ISSN:
Container-title:2024 IEEE 37th International Symposium on Computer-Based Medical Systems (CBMS)
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Author:

Gonzalez-Perez Ruben¹,Lopez-Tiro Francisco¹,Reyes-Amezcua Ivan²,Falcon-Morales Eduardo¹,Rodríguez-Guéant Rosa-Maria³,Hubert Jacques⁴,Daudon Michel⁵,Ochoa-Ruiz Gilberto¹,Daul Christian⁶

Affiliation:

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

2. CINVESTAV,Computer Sciences Department,Guadalajara,Mexico

3. INSERM and Université de Lorraine,NGERE, UMR S 1256,Vandoeuvre-lès-Nancy,France,54500

4. CHRU Nancy, Service d’urologie de Brabois,Vandoeuvre-lès-Nancy,France

5. Hôpital Tenon, AP-HP,Paris,France

6. UMR 7039, Université de Lorraine and CNRS,CRAN,Nancy,France

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10600689/10600715/10600716.pdf?arnumber=10600716

Reference20 articles.

1. Artificial Intelligence-Based Clinical Decision Support Systems Using Advanced Medical Imaging and Radiomics

2. Where do we stand in AI for endoscopic image analysis? Deciphering gaps and future directions

3. Boosting Kidney Stone Identification in Endoscopic Images Using Two-Step Transfer Learning

4. Comprehensive morpho-constitutional analysis of urinary stones improves etiological diagnosis and therapeutic strategy of nephrolithiasis

5. On the In Vivo Recognition of Kidney Stones Using Machine Learning

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