Home ›
Entities
› academia
› Feasibility of Monte Carlo dropout‐based uncertainty maps to evaluate deep learning‐based synthetic CTs for adaptive proton therapy
Feasibility of Monte Carlo dropout‐based uncertainty maps to evaluate deep learning‐based synthetic CTs for adaptive proton therapy
Research article (Medical Physics, 2023) · cited 29× · AI/ML
Feasibility of Monte Carlo dropout‐based uncertainty maps to evaluate deep learning‐based synthetic CTs for adaptive proton therapy
Summary
Feasibility of Monte Carlo dropout‐based uncertainty maps to evaluate deep learning‐based synthetic CTs for adaptive proton therapy is a scholarly article[1].
Key Facts
Feasibility of Monte Carlo dropout‐based uncertainty maps to evaluate deep learning‐based synthetic CTs for adaptive proton therapy's instance of is recorded as scholarly article[2].
References
Programmatic citations — every numbered marker resolves to a verifiable graph row below.
Use these citations when quoting this entity in research, articles, AI prompts, or wherever provenance matters. We aggregate Wikidata + Wikipedia + authoritative open-data sources; the stitched, scored, cross-referenced view is what 4ort.xyz contributes.
APA4ort.xyz Knowledge Graph. (2026). Feasibility of Monte Carlo dropout‐based uncertainty maps to evaluate deep learning‐based synthetic CTs for adaptive proton therapy. Retrieved May 24, 2026, from https://4ort.xyz/entity/feasibility-of-monte-carlo-dropoutbased-uncertainty-maps-to-evaluate-deep-learningbased-synthetic-cts-for-adaptive-proto
MLA“Feasibility of Monte Carlo dropout‐based uncertainty maps to evaluate deep learning‐based synthetic CTs for adaptive proton therapy.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/feasibility-of-monte-carlo-dropoutbased-uncertainty-maps-to-evaluate-deep-learningbased-synthetic-cts-for-adaptive-proto.
BibTeX@misc{4ortxyz_feasibility-of-monte-carlo-dropoutbased-uncertainty-maps-to-evaluate-deep-learningbased-synthetic-cts-for-adaptive-proto_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Feasibility of Monte Carlo dropout‐based uncertainty maps to evaluate deep learning‐based synthetic CTs for adaptive proton therapy}}, year = {2026}, url = {https://4ort.xyz/entity/feasibility-of-monte-carlo-dropoutbased-uncertainty-maps-to-evaluate-deep-learningbased-synthetic-cts-for-adaptive-proto}, note = {Accessed: 2026-05-24}}
LLM promptAccording to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Feasibility of Monte Carlo dropout‐based uncertainty maps to evaluate deep learning‐based synthetic CTs for adaptive proton therapy — https://4ort.xyz/entity/feasibility-of-monte-carlo-dropoutbased-uncertainty-maps-to-evaluate-deep-learningbased-synthetic-cts-for-adaptive-proto (retrieved 2026-05-24)