Exploring the impact of network depth on 3D U-Net-based dose prediction for cervical cancer radiotherapy
The results reveal that the network model with a depth of 5 exhibits superior performance, albeit at the expense of the longest training time and maximum computational memory in the three models. A small server with two NVIDIA GeForce RTX 3090 GPUs with 24 G of memory was employed for this training. For the 3D U-Net model with a depth of more than 5 cannot be supported due to insufficient training memory, the 3D U-Net neural network with a depth of 5 is the commonly used and optimal choice for small servers.
Authors
- Mingqing Wang (ORCID: https://orcid.org/0000-0003-1933-1566)
- Xile Zhang (ORCID: https://orcid.org/0009-0001-6344-5707)
- Ruijie Yang (ORCID: https://orcid.org/0000-0002-2836-8005)
- Yuxi Pan
Institutions
- Peking University (CN)
- Peking University Third Hospital (CN)
Publication Details
- Journal
- Frontiers in Oncology
- Published
- 2024-09-16
- DOI
- https://doi.org/10.3389/fonc.2024.1433225
- Citations
- 2
- Primary Topic
- Advanced Radiotherapy Techniques
- Type
- article
- Field-Weighted Citation Impact
- 1.22
Funders
- Beihang University
- Chinese Academy of Medical Sciences
- Peking University
- Peking University Third Hospital
- National Key Research and Development Program of China