arXiv (All)
open access
AtlasPatch: Scalable Foundation Model-based Tissue Detection and Patch Extraction for Computational Pathology
Whole-slide image (WSI) preprocessing, including tissue detection and patch extraction, is critical computational pathology, yet remains a major bottleneck for large-scale workflows. Existing methods often rely either on threshold-based heuristics that are sensitive to staining variations, tissue fr
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
- AtlasPatch: Scalable Foundation Model-based Tissue Detection and Patch Extraction for Computational Pathology#920270
- YoLeTooth: A Unified Framework for Joint Tooth Segmentation and Periapical Lesion Detection in Panoramic Radiographs.#315453
- LeCor: Learning to Be Corrected by Meta-Learned Test-Time Training for Interactive 3D Lung-Tumour Segmentation#987219
- ConPro: Contrast Projection Pretraining for Label-Efficient Vessel Segmentation in DSA Sequences#1078114
- Study of Cardiac Lesions Angiogenesis by 68Ga-NODAGA-RGD Cardiac PET#899633
Record · ID 924501
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