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End-to-End Cell Detection via Instance-aware Graph Modeling — arXiv:2609.15354

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.15354
License (as archived): CC0-1.0
Archived: 2026-10-15T22:53:46.335Z
SHA-256 of the archived text: 61caabaedf44e7c3039bf5d6f1bbe3ef798575e28d5b58a8f810e19b7cbdbf5f
Stable URL of this page: https://metraipolis.com/documento/dc_ebef59b09bb3

How to cite

End-to-End Cell Detection via Instance-aware Graph Modeling — arXiv:2609.15354 — arXiv (en), https://arxiv.org/abs/2609.15354 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_ebef59b09bb3 (Metraipolis archive dc_ebef59b09bb3, sha256 61caabaedf44e7c3039bf5d6f1bbe3ef798575e28d5b58a8f810e19b7cbdbf5f)

BibTeX

@misc{dcebef59b09bb3,
  title = {End-to-End Cell Detection via Instance-aware Graph Modeling — arXiv:2609.15354},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.15354},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_ebef59b09bb3; sha256 61caabaedf44e7c3039bf5d6f1bbe3ef798575e28d5b58a8f810e19b7cbdbf5f},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.15354. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Accurate cell detection and classification are crucial for pathological analysis, directly affecting diagnostic accuracy and treatment planning. To capture complex cellular interactions beyond visual appearance within the tumor microenvironment, several approaches have employed graph neural networks to model spatial and relational patterns among cell nuclei, yielding promising results. However, these methods typically adopt a two-stage paradigm of visual extraction followed by relational modeling, which necessitates separate tuning for each stage, thereby increasing pipeline complexity and hindering end-to-end joint optimization. In this paper, we propose an end-to-end framework for cell detection and classification that jointly models patch-level visual representations and instance-level interactions, which incorporates a dynamic graph construction module and an instance-aware graph network. Specifically, the graph construction module dynamically builds the graph structure using learnable queries derived from patch-level features as cell instance representations, with adjacency defined by integrating feature similarity and spatial distances. The instance-aware graph network performs adaptive instance filtering and feature reorganization, aggregating them over the cell graph into a topological latent state for a selective state-space transition driven by visual cues, fusing appearance and relational evidence. When evaluated on multiple datasets with different staining protocols for cell and nucleus detection, our method significantly outperforms existing approaches in both detection and classification performance. The code will be released at this https URL .

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-15 in cs.AI).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV) ; Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2609.15354 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-15T22:29:03.844Z dalla pagina https://arxiv.org/catchup/cs.AI/2026-09-15?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).