Comparing feature fusion strategies for Deep Learning-based kidney stone identification

• URL: http://arxiv.org/abs/2206.00069v1
• Date: Tue, 31 May 2022 19:27:54 GMT
• Title: Comparing feature fusion strategies for Deep Learning-based kidney stone identification
• Authors: Elias Villalvazo-Avila, Francisco Lopez-Tiro, Daniel Flores-Araiza, Gilberto Ochoa-Ruiz, Jonathan El-Beze, Jacques Hubert, Christian Daul
• Abstract summary: Our approach was specifically designed to mimic the morpho-constitutional analysis used by urologists to visually classify kidney stones. Deep feature fusion strategies improved the results of single view extraction backbone models by more than 10% in terms of precision of the kidney stones classification.
• Score: 0.9236074230806579
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This contribution presents a deep-learning method for extracting and fusing image information acquired from different viewpoints with the aim to produce more discriminant object features. Our approach was specifically designed to mimic the morpho-constitutional analysis used by urologists to visually classify kidney stones by inspecting the sections and surfaces of their fragments. Deep feature fusion strategies improved the results of single view extraction backbone models by more than 10\% in terms of precision of the kidney stones classification.

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