Assessing kidney stone composition using smartphone microscopy and deep neural networks

Author:

Onal Ege Gungor1ORCID,Tekgul Hakan2

Affiliation:

1. Department of Bioengineering University of Illinois at Urbana‐Champaign Champaign Illinois USA

2. Department of Computer Engineering Georgia Institute of Technology Atlanta Georgia USA

Publisher

Wiley

Subject

Religious studies,Cultural Studies

Cited by 8 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. Transforming urinary stone disease management by artificial intelligence-based methods: A comprehensive review;Asian Journal of Urology;2023-07

2. Deep Prototypical-Parts Ease Morphological Kidney Stone Identification and are Competitively Robust to Photometric Perturbations;2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW);2023-06

3. The technological future of percutaneous nephrolithotomy: a Young Academic Urologists Endourology and Urolithiasis Working Group update;Current Opinion in Urology;2023-01-09

4. Theranostic roles of machine learning in clinical management of kidney stone disease;Computational and Structural Biotechnology Journal;2023

5. Vision Transformer for Kidney Stone Detection;Future Data and Security Engineering. Big Data, Security and Privacy, Smart City and Industry 4.0 Applications;2023

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