Technical note: Determining the applicability of a clinical knowledge‐based learning model via prospective outlier detection-Reference-Cited by-同舟云学术

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Technical note: Determining the applicability of a clinical knowledge‐based learning model via prospective outlier detection

Published:2022-02-22 Issue:4 Volume:49 Page:2193-2202
ISSN:0094-2405
Container-title:Medical Physics
language:en
Short-container-title:Medical Physics

Author:

Zhang Jiahan¹,Sheng Yang²,Wolf Jonathan¹,Kayode Oluwatosin¹,Bradley Jeffrey¹,Ge Yaorong³,Wu Q Jackie²,Yang Xiaofeng¹,Liu Tian¹,Roper Justin¹

Affiliation:

1. Department of Radiation Oncology Emory University Atlanta Georgia USA

2. Department of Radiation Oncology Duke University Durham North Carolina USA

3. Department of Software and Information Systems The University of North Carolina at Charlotte Charlotte North Carolina USA

Publisher

Wiley

Subject

General Medicine

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/mp.15516

Reference29 articles.

1. Knowledge‐based planning for intensity‐modulated radiation therapy: A review of data‐driven approaches

2. Fully automated treatment planning for head and neck radiotherapy using a voxel-based dose prediction and dose mimicking method

3. Automatic IMRT planning via static field fluence prediction (AIP-SFFP): a deep learning algorithm for real-time prostate treatment planning

4. Deep learning-based inverse mapping for fluence map prediction

5. A hierarchical deep reinforcement learning framework for intelligent automatic treatment planning of prostate cancer intensity modulated radiation therapy

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