output
20192025
most citedAssessing deep learning methods for the identification of kidney stones in endoscopic images

48 citations

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Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.

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cs.CV2022★ 17 cited

Deep morphological recognition of kidney stones using intra-operative endoscopic digital videos

Vincent Estrade, Michel Daudon, Emmanuel Richard +5

The collection and the analysis of kidney stone morphological criteria are essential for an aetiological diagnosis of stone disease. However, in-situ LASER-based fragmentation of u…