Assessment of cell nuclei AI foundation models in kidney pathology

Q: How reproducible is "Assessment of cell nuclei AI foundation models in kidney pathology"?

Estimated time to first reproduction: a few days. Risk flags: No repository-level reproducibility signals are currently available, Estimate is based on paper-only reproduction flow. No direct maintained implementation was found. Use the paper PDF and citation graph to design a baseline reproduction.

Junlin Guo, Siqi Lu, Can Cui, Ruining Deng, Tianyuan Yao, Z H Tao, Yuan Lin, Marilyn Lionts, Quan Liu, Juming Xiong, Yu Wang, Shilin Zhao, Chin‐Kuo Chang, Mitchell Wilkes, Mengmeng Yin, Haichun Yang, Yuankai Huo

Published: Apr 10, 2025

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Evidence: Inferred

Domain fit: Niche / domain-specific

Verified repos: 0

No strong AI-core implementation/artifact signals were detected from current providers.

Time to first repro: a few days

2 risk flags

DOI Publisher

Cell nuclei instance segmentation is a crucial task in digital kidney pathology. Traditional automatic segmentation methods often lack generalizability when applied to unseen datasets. Recently, the success of foundation models (FMs) has provided a more generalizable solution, potentially enabling the segmentation of any cell type. In this study, we perform a large-scale evaluation of three widely used state-of-the-a ...

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rt (SOTA) cell nuclei foundation models-Cellpose, StarDist, and CellViT. Specifically, we created a highly diverse evaluation dataset consisting of 2,542 kidney whole slide images (WSIs) collected from both human and rodent sources, encompassing various tissue types, sizes, and staining methods. To our knowledge, this is the largest-scale evaluation of its kind to date. Our quantitative analysis of the prediction distribution reveals a persistent performance gap in kidney pathology. Among the evaluated models, CellViT demonstrated superior performance in segmenting nuclei in kidney pathology. However, none of the foundation models are perfect; a performance gap remains in general nuclei segmentation for kidney pathology.

Technical details

Canonical key: doi-10.1117_12.3047301

Cache status: Fresh

Generated at: Jun 19, 2026, 7:34 PM

Artifact coverage: sparse

HF provider: ok (mixed)

PWC source used: No

LLM status: not_generated

LLM model: n/a

LLM generated: Unknown

LLM content type: n/a

HF policy: hf-relevance-v27

context only

Benchmarks: missing

Time to repro: a few days

2 risk flags

Results & Benchmarks

Freshness tier: cold

Direct + Inferred Evidence

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Cell nuclei instance segmentation is a crucial task in digital kidney pathology.

Implementation Evidence Summary

Confidence: low

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Reproduction Risks

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Hardware Notes

Expect multi-day setup/compute for meaningful reproduction based on current guidance.

Evidence disclosure

Evidence graph: 2 refs, 1 links.

Utility signals: depth 65/100, grounding 58/100, status medium.

Implementation Status

No verified maintained repo

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No direct maintained implementation was found. Use the paper PDF and citation graph to design a baseline reproduction.
Start from related paper: ИСПОЛЬЗОВAНИЕ ПОТЕНЦИAЛA СОЦИAЛЬНЫХ ПAРТНЕРОВ В ПОДГОТОВКЕ БУДУЩИХ ПЕДAГОГОВ.
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Time to first repro: a few days

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Last checked: Jun 19, 2026

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Foundation (evidence), Pathology, Computer science, Medicine, Physical Sciences

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Foundation (evidence) Pathology Computer science Medicine Physical Sciences Modeling and Simulation