Unsupervised Learning of Cell Instances with Generative Routing Pyramids

Excerpt

arXiv:2608.16810v1 Announce Type: cross Abstract: Identifying and representing object instances such as cells or nuclei is a common task in microscopy image analysis. Established machine learning workflows typically use supervised detection or segmentation followed by feature extraction or classification, which requires manual annotations and treats instance segmentation and cell representation as separate stages. We describe a new unsupervised method for cell instance segmentation and phenotypi