Unsupervised machine learning model for detecting anomalous volumetric modulated arc therapy plans for lung cancer patients
The proposed multi-task AE model using two-dimensional (2D) feature maps can effectively detect anomalies in radiotherapy plans for lung cancer patients. Compared to the other existing AE models, the multi-task AE is more accurate and efficient. The proposed model provides a feasible way to carry out automated anomaly detection of VMAT plans in radiotherapy.
Authors
- Jiawen Shang
- Zhiqiang Liu (ORCID: https://orcid.org/0000-0003-2135-8581)
- Peng Huang (ORCID: https://orcid.org/0000-0002-4379-8871)
- Hui Yan (ORCID: https://orcid.org/0000-0003-0175-7884)
- Jianrong Dai (ORCID: https://orcid.org/0000-0003-3542-4148)
- Zhihui Hu
- Yuhan Fan (ORCID: https://orcid.org/0009-0003-2645-4182)
Institutions
- Chinese Academy of Medical Sciences & Peking Union Medical College (CN)
Publication Details
- Journal
- Frontiers in Big Data
- Published
- 2024-10-03
- DOI
- https://doi.org/10.3389/fdata.2024.1462745
- Citations
- 2
- Primary Topic
- Anomaly Detection Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.69
Funders
- National Natural Science Foundation of China