An Intentionally-Overfitting Automatic Contour Quality Assurance Tool for Auto-Segmentation in MR-Guided Online Adaptive Radiotherapy

Y. Zhang; M. Zarenia; Christina Sarosiek; A. Amjad; Rory Conlin; Bradley A. Erickson; William A. Hall; E.S. Paulson

doi:10.1016/j.ijrobp.2024.07.471

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An Intentionally-Overfitting Automatic Contour Quality Assurance Tool for Auto-Segmentation in MR-Guided Online Adaptive Radiotherapy

Y. Zhang M. Zarenia Christina Sarosiek A. Amjad Rory Conlin Bradley A. Erickson William A. Hall E.S. Paulson

Year: 2024 Journal: International Journal of Radiation Oncology*Biology*Physics Vol: 120 (2)Pages: e208-e208 Publisher: Elsevier BV

DOI: 10.1016/j.ijrobp.2024.07.471

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Keywords:

Overfitting Quality assurance Computer science Segmentation Artificial intelligence Market segmentation Computer vision Radiation therapy Medicine Radiology Artificial neural network Business

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Advanced Radiotherapy Techniques

Physical Sciences → Physics and Astronomy → Radiation

Medical Imaging Techniques and Applications

Health Sciences → Medicine → Radiology, Nuclear Medicine and Imaging

Radiomics and Machine Learning in Medical Imaging

Health Sciences → Medicine → Radiology, Nuclear Medicine and Imaging

An Intentionally-Overfitting Automatic Contour Quality Assurance Tool for Auto-Segmentation in MR-Guided Online Adaptive Radiotherapy

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