arXiv (All)
open access
Comprehensive Evaluation and Fine-Tuning of Foundational Cell Nuclei Segmentation Models in Renal Pathology
Accurate nuclei instance segmentation is essential for quantitative renal pathology, yet general-purpose models often struggle with low contrast, dense nuclei, complex morphology, and strong background staining. In this work, we extended a human-in-the-loop framework by combining 5,901 foundation-mo
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Deep visual multi-omics profiling links morphology and molecular programs in clear cell renal cell carcinoma.#785668
- StainBridge: Stain-Aware Pairwise Registration of Serial Renal Biopsy Whole-Slide Images Across Structural and Immunohistochemical Stains#1123482
- Hybrid Densenet-U-Net Framework for Automated Grading of Renal Cell Carcinoma#1081892
- Evaluating Cell AI Foundation Models in Kidney Pathology with Human-in-the-Loop Enrichment#1117207
- Streamline pathology foundation model by cross-magnification distillation#1326145
Record · ID 1217086
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive