2022/06/01 by Daniel Flores-Araiza, Francisco Lopez-Tiro, Flores-Araiza, Daniel +11
Arts and Humanities · Medicine · #Artificial Intelligence (cs.AI) #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Kidney Stones and Urolithiasis Treatments #Machine Learning (cs.LG) #Paleopathology and ancient diseases
paper · pdf · doi:10.48550/arxiv.2206.00252
openalex publication_date 2022/06/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Identifying the type of kidney stones can allow urologists to determine their formation cause, improving the early prescription of appropriate treatments to diminish future relapses. However, currently, the associated ex-vivo diagnosis (known as morpho-constitutional analysis, MCA) is time-consuming, expensive, and requires a great deal of experience, as it requires a visual analysis component that is highly operator dependant. Recently, machine learning methods have been developed for in-vivo endoscopic stone recognition. Shallow methods have been demonstrated to be reliable and interpretable but exhibit low accuracy, while deep learning-based methods yield high accuracy but are not explainable. However, high stake decisions require understandable computer-aided diagnosis (CAD) to suggest a course of action based on reasonable evidence, rather than merely prescribe one. Herein, we investigate means for learning part-prototypes (PPs) that enable interpretable models. Our proposal suggests a classification for a kidney stone patch image and provides explanations in a similar way as those used on the MCA method.