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Kailyn Stenhouse

Showing results (1-10 of 7) with videos related to

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Frontiers in Oncology|March 22, 2021
Development of a Machine Learning Model for Optimal Applicator Selection in High-Dose-Rate Cervical BrachytherapyKailyn Stenhouse, Michael Roumeliotis, Philip Ciunkiewicz, et al.
Frontiers in Oncology|July 23, 2021
Corrigendum: Development of a Machine Learning Model for Optimal Applicator Selection in High-Dose-Rate Cervical BrachytherapyKailyn Stenhouse, Michael Roumeliotis, Philip Ciunkiewicz, et al.
Medical Physics|February 23, 2021
Technical Note: A standardized automation framework for monitoring institutional radiotherapy protocol complianceSarah Quirk, Jordan Lovis, Kailyn Stenhouse, et al.
Medical Physics|April 20, 2022
Assessment of tissue toxicity risk in breast radiotherapy using Bayesian networksPhilip Ciunkiewicz, Michael Roumeliotis, Kailyn Stenhouse, et al.
Biomedical Physics & Engineering Express|December 6, 2024
Development of a machine learning tool to predict deep inspiration breath hold requirement for locoregional right-sided breast radiation therapy patientsFletcher Barrett, Sarah Quirk, Kailyn Stenhouse, et al.
Medical Physics|May 22, 2025
A simulated annealing-based Bayesian network structure optimization framework for late morbidity prediction with a large prospective datasetKailyn Stenhouse, Philip McGeachy, Sofia Spampinato, et al.
Brachytherapy|March 27, 2024
Prospective validation of a machine learning model for applicator and hybrid interstitial needle selection in high-dose-rate (HDR) cervical brachytherapyKailyn Stenhouse, Michael Roumeliotis, Philip Ciunkiewicz, et al.
Pageof 1

Showing results (1-10 of 7) with videos related to

Sort By:
Pageof 1
Frontiers in Oncology|March 22, 2021
Development of a Machine Learning Model for Optimal Applicator Selection in High-Dose-Rate Cervical BrachytherapyKailyn Stenhouse, Michael Roumeliotis, Philip Ciunkiewicz, et al.
Frontiers in Oncology|July 23, 2021
Corrigendum: Development of a Machine Learning Model for Optimal Applicator Selection in High-Dose-Rate Cervical BrachytherapyKailyn Stenhouse, Michael Roumeliotis, Philip Ciunkiewicz, et al.
Medical Physics|February 23, 2021
Technical Note: A standardized automation framework for monitoring institutional radiotherapy protocol complianceSarah Quirk, Jordan Lovis, Kailyn Stenhouse, et al.
Medical Physics|April 20, 2022
Assessment of tissue toxicity risk in breast radiotherapy using Bayesian networksPhilip Ciunkiewicz, Michael Roumeliotis, Kailyn Stenhouse, et al.
Biomedical Physics & Engineering Express|December 6, 2024
Development of a machine learning tool to predict deep inspiration breath hold requirement for locoregional right-sided breast radiation therapy patientsFletcher Barrett, Sarah Quirk, Kailyn Stenhouse, et al.
Medical Physics|May 22, 2025
A simulated annealing-based Bayesian network structure optimization framework for late morbidity prediction with a large prospective datasetKailyn Stenhouse, Philip McGeachy, Sofia Spampinato, et al.
Brachytherapy|March 27, 2024
Prospective validation of a machine learning model for applicator and hybrid interstitial needle selection in high-dose-rate (HDR) cervical brachytherapyKailyn Stenhouse, Michael Roumeliotis, Philip Ciunkiewicz, et al.
Pageof 1