Home ›
Entities
› academia
› Comparison of computed tomography image features extracted by radiomics, self-supervised learning and end-to-end deep learning for outcome prediction of oropharyngeal cancer
Comparison of computed tomography image features extracted by radiomics, self-supervised learning and end-to-end deep learning for outcome prediction of oropharyngeal cancer
Research article (Physics and Imaging in Radiation Oncology, 2023) · cited 10× · AI/ML
Comparison of computed tomography image features extracted by radiomics, self-supervised learning and end-to-end deep learning for outcome prediction of oropharyngeal cancer
Summary
Comparison of computed tomography image features extracted by radiomics, self-supervised learning and end-to-end deep learning for outcome prediction of oropharyngeal cancer is a scholarly article[1].
Key Facts
Comparison of computed tomography image features extracted by radiomics, self-supervised learning and end-to-end deep learning for outcome prediction of oropharyngeal cancer'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). Comparison of computed tomography image features extracted by radiomics, self-supervised learning and end-to-end deep learning for outcome prediction of oropharyngeal cancer. Retrieved May 24, 2026, from https://4ort.xyz/entity/comparison-of-computed-tomography-image-features-extracted-by-radiomics-self-supervised-learning-and-end-to-end-deep-lea
MLA“Comparison of computed tomography image features extracted by radiomics, self-supervised learning and end-to-end deep learning for outcome prediction of oropharyngeal cancer.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/comparison-of-computed-tomography-image-features-extracted-by-radiomics-self-supervised-learning-and-end-to-end-deep-lea.
BibTeX@misc{4ortxyz_comparison-of-computed-tomography-image-features-extracted-by-radiomics-self-supervised-learning-and-end-to-end-deep-lea_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Comparison of computed tomography image features extracted by radiomics, self-supervised learning and end-to-end deep learning for outcome prediction of oropharyngeal cancer}}, year = {2026}, url = {https://4ort.xyz/entity/comparison-of-computed-tomography-image-features-extracted-by-radiomics-self-supervised-learning-and-end-to-end-deep-lea}, note = {Accessed: 2026-05-24}}
LLM promptAccording to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Comparison of computed tomography image features extracted by radiomics, self-supervised learning and end-to-end deep learning for outcome prediction of oropharyngeal cancer — https://4ort.xyz/entity/comparison-of-computed-tomography-image-features-extracted-by-radiomics-self-supervised-learning-and-end-to-end-deep-lea (retrieved 2026-05-24)
