eScholarship
metadata only
When machine vision meets histology: A comparative evaluation of model architecture for classification of histology sections
Classification of histology sections in large cohorts, in terms of distinct regions of microanatomy (e.g., stromal) and histopathology (e.g., tumor, necrosis), enables the quantification of tumor composition, and the construction of predictive models of genomics and clinical outcome. To tackle the l
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
- Risk Stratification of Hepatocarcinogenesis Using a Deep Learning Based Clinical, Biological and Ultrasound Model in High-risk Patients#1232008
- The AI Prognostic Assessment and Pathological Basis Research of Early HCC After Minimally Invasive Treatment#1163850
- HCC Risk Prediction Model Based on Multimodal Data in Chronic Hepatitis B#1032605
- The Role of Artificial Intelligence in Endoscopic Diagnosis of Esophagogastric Junctional Adenocarcinoma:A Single Center, Case-control, Diagnostic Study#1229294
- Using Machine Learning to Identify Responders to TACE or HAIC for uHCC#873517
Record · ID 1263488
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