arXiv (All)
open access
Predicting Metastatic Risk from Primary Cancer Tissue Architecture via Distance-Aware Spatial Modeling
Predicting distant metastasis from the digital H & E slides of the primary tumor is a critical yet challenging task in computational pathology. Multiple Instance Learning (MIL) approaches can attend to subdomains in whole slide images (WSIs) that harbor features of pre-metastatic cancer regions. How
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
- Development of machine learning and nomogram models to predict lung metastasis and prognosticate survival in breast cancer.#258173
- Conserved Immune Topology Improves Pathology Foundation Model Generalization for Cross-Cancer MSI-H Prediction#972127
- [Hierarchical attention-driven multiple instance learning for clear cell renal cell carcinoma grading and staging in digital pathology].#236056
- Multimodal Al for Precision Diagnosis of Esophageal Cancer#813195
- Improving Clinical Target Volume Segmentation Accuracy using Anatomical Priors and Active Learning for the AGITG TOPGEAR Clinical Trial#925548
Record · ID 800089
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