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14 October 2024 |
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Article overview
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Interpretable Deep Learning Classifier by Detection of Prototypical Parts on Kidney Stones Images | | Daniel Flores-Araiza
; Francisco Lopez-Tiro
; Elias Villalvazo-Avila
; Jonathan El-Beze
; Jacques Hubert
; Gilberto Ochoa-Ruiz
; Cristian Daul
; | | Date: |
1 Jun 2022 | | 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. | | Source: | arXiv, 2206.00252 | | Services: | Forum | Review | PDF | Favorites | |
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