Open access
Sep 2026
A hybrid deep learning framework for automated treatment planning in cervical cancer radiotherapy
Abstract Background Radiation therapy treatment planning is a time‐consuming trial‐and‐error process, and the plan quality is heavily dependent on planners' experiences, resulting in a strong demand for automated planning methods that can rapidly generate uniformly high‐quality plans. Purpose This study aimed to develo...
Hai-Yan Jiang, Zeng-Tai Yuan, Zi-Hao Liu et al.
· Journal of Applied Clinical... · 0 citations