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English(EN) Anatomy-Change-Aware Bidirectional Selective State-Space Memory for Clinically Deployed Thoracic Radiotherapy Auto-Contouring

新型AI模型DAMM-Net++增强胸部放疗自动勾画

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了DAMM-Net++，这是一种新颖的2.5D架构，旨在改进胸部放疗中的自动勾画。该系统通过引入解剖结构变化感知双向选择状态空间记忆，解决了切片间表面不连贯以及在小型、低对比度目标上失效等挑战。该模型在多个患者队列和读者研究中表现出强大的性能，显著减少了勾画时间并提高了准确性，其不确定性头部为临床分诊提供了校准的置信度。 AI

影响 这一发展可能显著提高放疗计划的效率和准确性，从而可能带来更好的患者治疗效果。

排序理由 该集群包含一篇详细介绍用于特定医疗应用的AI新模型的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新型AI模型DAMM-Net++增强胸部放疗自动勾画

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该集群包含一篇详细介绍用于特定医疗应用的AI新模型的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CV TIER_1 English(EN) · Galib Ahmed, Istiak Ahmed, Aritra Islam Saswato, Asib Mostakim Fony, Kazi Shahriar Sanjid, Md. Tanzim Hossain, Md. Anwarul Islam, Md. Nishan Khan, Md. Misbah Khan, Labiba Faiza Karim, Jobaer Rahman, S M Hasibul Hoque, Rahnuma Shahrin Rista, Kamruzzaman R… ·

    面向临床部署的胸部放疗自动轮廓勾画的解剖结构变化感知双向选择性状态空间记忆

    arXiv:2609.16036v1 Announce Type: cross Abstract: We developed DAMM-Net++, a 2.5D architecture for thoracic OAR and target volume segmentation that addresses three persistent challenges in radiotherapy auto-contouring: inter-slice surface incoherence, systematic failure on small …

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