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English(EN) LETT-NeXt: A Lightweight RECIST-Guided Model for 3D CT Lesion Segmentation

LETT-NeXt模型通过RECIST指导增强3D CT病灶分割

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了LETT-NeXt，这是一种用于分割CT扫描中3D病灶的轻量级模型。该模型整合了RECIST（实体瘤疗效评价标准）标记作为指导，增强了其预测病灶掩码的能力。LETT-NeXt在CVPR 2026 Foundation Models for Pan-cancer Segmentation in CT Images竞赛中取得了有竞争力的结果，展示了高效的推理时间和低内存使用量。 AI

影响 该模型可以提高临床环境中肿瘤反应评估的准确性和效率。

排序理由 该集群描述了一篇详细介绍用于医学图像分割的新模型的论文。

在 arXiv cs.CV 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

LETT-NeXt模型通过RECIST指导增强3D CT病灶分割

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该集群描述了一篇详细介绍用于医学图像分割的新模型的论文。
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报道来源 [2]

  1. arXiv cs.CV TIER_1 English(EN) · Sebastian Aas, Elias Stenhede, Arian Ranjbar ·

    LETT-NeXt：一种轻量级的、由RECIST引导的3D CT病灶分割模型

    arXiv:2606.30108v1 Announce Type: new Abstract: RECIST diameter measurements are widely used for tumor response assessment, but they provide only a limited 2D description of lesion extent. We present LETT-NeXt, a lightweight RECIST-guided model that predicts 3D lesion masks from …

  2. arXiv cs.CV TIER_1 English(EN) · Arian Ranjbar ·

    LETT-NeXt：一种轻量级的、由RECIST引导的3D CT病灶分割模型

    RECIST diameter measurements are widely used for tumor response assessment, but they provide only a limited 2D description of lesion extent. We present LETT-NeXt, a lightweight RECIST-guided model that predicts 3D lesion masks from CT volumes and RECIST markers for the CVPR 2026 …

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