DigitalCommons@University of Nebraska
metadata only
Non-destructive Automated Classification of Human and Large Mammal Long Bone Fragments: A Deep Learning Approach Using Micro-CT Histomorphology and Grad-Cam Interpretation
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-learning-based detection of open-apex teeth on panoramic radiographs using YOLO models#324816
- Multimodal transformer-enhanced deep learning for accurate assessment of mandibular third molar–inferior alveolar canal contact using panoramic radiography and CBCT slices.#617123
- Patient-Level, Leakage-Aware Deep Learning for Cross-Center Periapical Radiograph Classification#1084634
- Automatic and quantitative measurement of alveolar bone level in OCT images using deep learning#654928
- Database on bending response and geometry of normal and pathological mouse femurs during early growth#499407
Record · ID 429886
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