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Detection of early onset of fibrosis is critical to detecting long term damage to identify potential loss of organ function. While formal grading systems for fibrosis have been established, we argue that a quantitative analysis of fibrosis patterns will improve diagnostic quality and help to standardise clinical reporting. Here we are using deep learning to identify elementary fibrosis patterns. Subsequently, a graphical model is utilised to model the spatial organisation of the fibrosis patterns. Our experimental results demonstrated that this approach correlates well with established clinical grading. The presented method holds the potential to be applied to histology in other organs (e.g. kidney).

Original publication

DOI

10.1007/978-3-030-87237-3_21

Type

Conference paper

Publication Date

01/01/2021

Volume

12908 LNCS

Pages

217 - 226