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Simultaneous Semantic and Instance Segmentation for Colon Nuclei Identification and Counting
[article]
2022
arXiv
pre-print
We address the problem of automated nuclear segmentation, classification, and quantification from Haematoxylin and Eosin stained histology images, which is of great relevance for several downstream computational pathology applications. In this work, we present a solution framed as a simultaneous semantic and instance segmentation framework. Our solution is part of the Colon Nuclei Identification and Counting (CoNIC) Challenge. We first train a semantic and instance segmentation model
arXiv:2203.00157v2
fatcat:h7vstvvorvddfmwkgpxzw5isei