Iowa State University Digital Repository
metadata only
Comparing windowing methods in 2D and 3D convolutional neural networks to classify brain midline shift
analyticalcomputed-tomographycomputer-aided-engineering-and-designconvolutional-neural-networksmechanical-engineeringmedicine-and-health-sciencestraumatic-brain-injury
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
- Common Blood Flow Changes across Visual Tasks: II. Decreases in Cerebral Cortex#485552
- Epileptic Classification With Deep-Transfer-Learning-Based Feature Fusion Algorithm#478107
- Conditional Flow Matching for Cross-Field MRI Harmonisation#820954
- Urban Eye: transformación del monitoreo pasivo en analítica predictiva mediante Deep Learning y computación perimetral#423828
- Neuron-Level Architecture Growth: A Controlled Evaluation for EEG Time-Series Decoding#1118953
Record · ID 334303
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