Point-supervised Single-cell Segmentation via Collaborative Knowledge Sharing. (arXiv:2304.10671v1 [cs.CV])

Despite their superior performance, deep-learning methods often suffer from
the disadvantage of needing large-scale well-annotated training data. In
response, recent literature has seen a proliferation of efforts aimed at
reducing the annotation burden. This paper focuses on a weakly-supervised
training setting for single-cell segmentation models, where the only available
training label is the rough locations of individual cells. The specific problem
is of practical interest due to the widely available nuclei counter-stain data
in biomedical literature, from which the cell …

annotation arxiv biomedical cells collaborative data general knowledge literature paper performance practical scale segmentation training training data weakly-supervised