NCBI PubMed Central
open access
From Linear-quadratic to Machine Learning in Radiobiological Modeling for Radiotherapy Planning: Resolving the Complexity-Utility Paradox with Hybrid Physics-informed Neural Networks.
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
- Beyond TG‑43: A PRISMA-based systematic review on model-based dose-calculation algorithms in brachytherapy.#265403
- Efficient deep learning-driven workflows for accelerated single-fraction radiation therapy planning of a solitary tumour in the lung.#1329804
- Generative AI, foundation models and large language models in radiation therapy physics: Clinical applications, challenges, and future directions.#439233
- Lightweight Transformer-based knowledge distillation framework for high-dimensional spatiotemporal radiomics in breast cancer risk prediction.#659621
- Machine Learning and Artificial Intelligence in Toxicology and Environmental Health#164185
Record · ID 1379989
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