Paper page - One Click per Cell Type Suffices: Training-free Group Interaction for Cell Instance Segmentation

Source: https://huggingface.co/papers/2605.29429

Abstract

Group Prompting enables efficient cell instance segmentation by leveraging per-type prompting through a training-free framework that uses multi-scale encoder features and recursive prompt expansion.

Cell instance segmentationmodels trained on cell-specific datasets suffer severe performance drops on out-of-distribution cell types, whileinteractive foundation modelsovercome this throughper-instance promptingat a cost that is prohibitively expensive for histopathology images containing hundreds to thousands of densely packed instances. We introduce Group Prompting, a new paradigm that shifts interactive segmentation from per-instance O(N) to per-type O(T), where a single click per cell type suffices to segment all instances of that type. Our key observation is that thefrozen image encoderof theSegment Anything Model(SAM) already clusters same-type cells in its feature space before any prompt is given. Exploiting this property, we proposeChain-of-Prompts(CoP), a training-free framework that recursively expands a single user click by (1) identifying reliable same-type locations throughnon-parametric gatingofmulti-scale encoder features, and (2) selecting the most spatially distant reliable point as the next prompt to maximize coverage. On three cell-type-annotated benchmarks, CoP with one click per type retains over 90% of per-instance performance and surpasses fully-supervised methods without any additional training. On four morphologically homogeneous benchmarks, a single click retains over 99%. Project Page: https://shjo-april.github.io/Chain-of-Prompts/

View arXiv pageView PDFProject pageGitHub3Add to collection

Get this paper in your agent:

hf papers read 2605\.29429

Don’t have the latest CLI?curl \-LsSf https://hf\.co/cli/install\.sh \| bash

Models citing this paper0

No model linking this paper

Cite arxiv.org/abs/2605.29429 in a model README.md to link it from this page.

Datasets citing this paper0

No dataset linking this paper

Cite arxiv.org/abs/2605.29429 in a dataset README.md to link it from this page.

Spaces citing this paper0

No Space linking this paper

Cite arxiv.org/abs/2605.29429 in a Space README.md to link it from this page.

Collections including this paper0

No Collection including this paper

Add this paper to acollectionto link it from this page.