English

Panoptic segmentation with highly imbalanced semantic labels

Image and Video Processing 2022-04-20 v4 Computer Vision and Pattern Recognition

Abstract

We describe here the panoptic segmentation method we devised for our participation in the CoNIC: Colon Nuclei Identification and Counting Challenge at ISBI 2022. Key features of our method are a weighted loss specifically engineered for semantic segmentation of highly imbalanced cell types, and a state-of-the art nuclei instance segmentation model, which we combine in a Hovernet-like architecture.

Cite

@article{arxiv.2203.11692,
  title  = {Panoptic segmentation with highly imbalanced semantic labels},
  author = {Josef Lorenz Rumberger and Elias Baumann and Peter Hirsch and Andrew Janowczyk and Inti Zlobec and Dagmar Kainmueller},
  journal= {arXiv preprint arXiv:2203.11692},
  year   = {2022}
}
R2 v1 2026-06-24T10:21:56.446Z