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Evaluating the Plausibility of Synthetic Images for Improving Automated Endoscopic Stone Recognition by Gonzalez-Perez, Ruben; Lopez-Tiro, Francisco; Reyes-Amezcua, Ivan; Falcon-Morales, Eduardo; Rodriguez-Gueant, Rosa-Maria; Hubert, Jacques; Daudon, Michel; Ochoa-Ruiz, Gilberto; Daul, Christian is a scholarly article available to read on EtoBox.

What is Evaluating the Plausibility of Synthetic Images for Improving Automated Endoscopic Stone Recognition about?

Currently, the Morpho-Constitutional Analysis (MCA) is the de facto approach for the etiological diagnosis of kidney stone formation, and it is an important step for establishing personalized treatment to avoid relapses. More recently, research has focused on performing such tasks intra-operatively, an approach known as Endoscopic Stone Recognition (ESR). Both methods rely on features observed in the surface and the section of kidney stones to separate the analyzed samples into several sub-groups. However, given the high intra-observer variability and the complex operating conditions found in ESR, there is a lot of interest in using AI for computer-aided diagnosis. However, current AI models require large datasets to attain a good performance and for generalizing to unseen distributions. This is a major problem as large labeled datasets are very difficult to acquire, and some classes of kidney stones are very rare. Thus, in this paper, we present a method based on diffusion as a way of augmenting pre-existing ex-vivo kidney stone datasets. Our aim is to create plausible diverse kidney stone images that can be used for pre-training models using ex-vivo data. We show that by mixing n

Author

Gonzalez-Perez, Ruben; Lopez-Tiro, Francisco; Reyes-Amezcua, Ivan; Falcon-Morales, Eduardo; Rodriguez-Gueant, Rosa-Maria; Hubert, Jacques; Daudon, Michel; Ochoa-Ruiz, Gilberto; Daul, Christian

Published

2024

Language

EN