Unsupervised Learning of Cell Instances with Generative Routing Pyramids
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 phenotypic classification from unlabeled microscopy images. Our method is based on reconstructing each image using a coarse-to-fine routing pyramid that associ
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Paper → model → repo connections mined from source citations (Tier-1 exact match).
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- FuzzySimilar title/name (fuzzy) · 84%GoogleCloudPlatform/generative-ai →
“Fuzzy title match (0.92): “Unsupervised Learning of Cell Instances with Generative Rout” ≈ “GoogleCloudPlatform/generative-ai””
- FuzzyOverlapping authors or contributors · 62%modular/modular →
“Shared author/contributor keys: liu”
- FuzzySimilar title/name (fuzzy) · 59%steven2358/awesome-generative-ai →
“Fuzzy title match (0.73): “Unsupervised Learning of Cell Instances with Generative Rout” ≈ “steven2358/awesome-generative-ai””
- FuzzySimilar title/name (fuzzy) · 59%aymericdamien/TopDeepLearning →
“Fuzzy title match (0.73): “Unsupervised Learning of Cell Instances with Generative Rout” ≈ “aymericdamien/TopDeepLearning””
- LinkedLinked via arxiv author · 85%Ziwen Liu →
“Unsupervised Learning of Cell Instances with Generative Routing Pyramids”
- LinkedLinked via arxiv author · 85%Martin Weigert →
“Unsupervised Learning of Cell Instances with Generative Routing Pyramids”