Cargando…
Cross-institutional outcome prediction for head and neck cancer patients using self-attention neural networks
In radiation oncology, predicting patient risk stratification allows specialization of therapy intensification as well as selecting between systemic and regional treatments, all of which helps to improve patient outcome and quality of life. Deep learning offers an advantage over traditional radiomic...
Autores principales: | Le, William Trung, Vorontsov, Eugene, Romero, Francisco Perdigón, Seddik, Lotfi, Elsharief, Mohamed Mortada, Nguyen-Tan, Phuc Felix, Roberge, David, Bahig, Houda, Kadoury, Samuel |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8873259/ https://www.ncbi.nlm.nih.gov/pubmed/35210482 http://dx.doi.org/10.1038/s41598-022-07034-5 |
Ejemplares similares
-
Combining dense elements with attention mechanisms for 3D radiotherapy dose prediction on head and neck cancers
por: Cros, Samuel, et al.
Publicado: (2022) -
Artificial intelligence to predict outcomes of head and neck radiotherapy
por: Bang, Chulmin, et al.
Publicado: (2023) -
Magnetic Resonance-Guided Radiation Therapy for Head and Neck Cancers
por: Lavigne, Danny, et al.
Publicado: (2022) -
Oligometastatic Head and Neck Cancer: Challenges and Perspectives
por: Bahig, Houda, et al.
Publicado: (2022) -
Conventionally fractionated large volume head and neck re-irradiation using multileaf collimator-based robotic technique: A feasibility study
por: Bahig, Houda, et al.
Publicado: (2020)