Deep Prototypical-Parts Ease Morphological Kidney Stone Identification and are Competitively Robust to Photometric Perturbations-Reference-Cited by-同舟云学术

Deep Prototypical-Parts Ease Morphological Kidney Stone Identification and are Competitively Robust to Photometric Perturbations

Published:2023-06 Issue: Volume: Page:
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Container-title:2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
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Author:

Flores-Araiza Daniel¹,Lopez-Tiro Francisco¹,El-Beze Jonathan²,Hubert Jacques²,Gonzalez-Mendoza Miguel¹,Ochoa-Ruiz Gilberto¹,Daul Christian³

Affiliation:

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

2. Service D’Urologie de Brabois,CHU Nancy,Nancy,France

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

Publisher

IEEE

Link

Reference28 articles.

4. Boosting kidney stone identification in endoscopic images using two-step transfer learning;lopez-tiro,2022

5. Interpretable deep learning classifier by detection of prototypical parts on kidney stones images;flores-araiza,2022

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