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English(EN) Dose-PlanNet: Physics Based Radiotherapy Dose Prediction with Deep Learning

深度学习模型可自动进行放射治疗规划

研究人员开发了 Dose-PlanNet,一个旨在自动化前列腺放射治疗规划复杂过程的深度学习模型。这种基于物理的 3D 架构可预测剂量分布,并在接受两种不同分次方案治疗的患者身上进行了评估。在实现可比的目标覆盖率的同时,Dose-PlanNet 在保护危及器官方面显示出统计学上的显著改进,并在两个治疗组的大部分自动化计划中达到了临床可接受标准。 AI

排序理由 该集群包含一篇在 arXiv 上发表的研究论文,详细介绍了一种用于特定科学应用的新型深度学习模型。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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深度学习模型可自动进行放射治疗规划

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该集群包含一篇在 arXiv 上发表的研究论文,详细介绍了一种用于特定科学应用的新型深度学习模型。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Ankit Bhattacharjee, Sougata Maity, Santam Chakraborty, Indranil Mallick ·

    Dose-PlanNet:基于物理的深度学习放射治疗剂量预测

    arXiv:2608.26901v1 Announce Type: cross Abstract: Automating prostate radiotherapy treatment planning is dosimetrically complex, particularly for extreme hypofractionated regimens. In this study, we introduce Dose-PlanNet, a physics-guided 3D deep learning architecture designed t…