arXiv cs.AI· Riqiang Gao, Simon Arberet, Martin Kraus, Han Liu, Wilko F. A. R. Verbakel, Dorin Comaniciu, Florin-Cristian Ghesu, Ali Kamen·· 12 小时前AI 评分35
生成式 AI 按用户偏好辅助放疗计划，性能超越 Varian RapidPlan
Demo: Generative AI helps Radiotherapy Planning with User Preference
AI 导读
研究提出一种仅依据用户自定义偏好风格预测 3D 剂量分布的生成模型，让放疗计划者可自行权衡危及器官（OARs）与计划靶区（PTVs）之间的取舍。该模型面向临床治疗计划系统集成设计，对比评估显示其在部分场景下的适应性与计划质量超过 Varian RapidPlan。
正文 · 原文
Abstract:Radiotherapy planning is a highly complex process that often varies significantly across institutions and individual planners. Most existing deep learning approaches for 3D dose prediction rely on reference plans as ground truth during training, which can inadvertently bias models toward specific planning styles or institutional preferences. In this study, we introduce a novel generative model that predicts 3D dose distributions based solely on user-defined preference flavors. These customizable preferences enable planners to prioritize specific trade-offs between organs-at-risk (OARs) and planning target volumes (PTVs), offering greater flexibility and personalization. Designed for seamless integration with clinical treatment planning systems, our approach assists users in generating high-quality plans efficiently. Comparative evaluations demonstrate that our method can surpasses the Varian RapidPlan model in both adaptability and plan quality in some scenarios.
| Comments: | Best paper in GenAI4Health at NeurIPS 2025 |
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2512.08996 [cs.CV] |
| (or arXiv:2512.08996v3 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2512.08996 arXiv-issued DOI via DataCite |
Submission history
From: Riqiang Gao [view email]
[v1]
Mon, 8 Dec 2025 16:49:21 UTC (4,698 KB)
[v2]
Thu, 24 Sep 2026 19:29:47 UTC (4,698 KB)
[v3]
Fri, 23 Oct 2026 15:42:29 UTC (4,681 KB)
来源：arXiv cs.AI · arxiv.org