Interpretable Deep Learning Classifier by Detection of Prototypical Parts on Kidney Stones Images

• URL: http://arxiv.org/abs/2206.00252v2
• Date: Thu, 2 Jun 2022 03:06:35 GMT
• Title: Interpretable Deep Learning Classifier by Detection of Prototypical Parts on Kidney Stones Images
• Authors: Daniel Flores-Araiza, Francisco Lopez-Tiro, Elias Villalvazo-Avila, Jonathan El-Beze, Jacques Hubert, Gilberto Ochoa-Ruiz, Christian Daul
• Abstract summary: Currently, the associated ex-vivo diagnosis (known as morpho-constitutional analysis, MCA) is time-consuming, expensive, and requires a great deal of experience. Machine learning methods have been developed for in-vivo endoscopic stone recognition. 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.
• Score: 0.9236074230806579
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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