arXiv (All)
open access
Leveraging Cell-level Spatial Transcriptomics as Weak Supervision for Nuclei Classification
Deep learning-based nuclei analysis in histopathology relies heavily on large-scale, expert-curated datasets that are costly and difficult to scale across organs. To address this, we propose a scalable framework leveraging cell-level spatial transcriptomics (ST) as an alternative supervision source
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
- UNICORN: a deep learning model for integrating multi-stain data in histopathology.#308819
- jsPCA: fast, scalable, and interpretable identification of spatial domains and variable genes across multi-slice and multi-sample spatial transcriptomics data.#636326
- Leveraging deep learning to infer continuous predictions from ordinal labels in medical imaging#121215
- SpaPheno: linking spatial transcriptomics to clinical phenotypes with interpretable machine learning.#209545
- Interpretable, flexible and spatially aware integration of multiple spatial transcriptomics datasets from diverse sources.#193594
Record · ID 1387038
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