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English(EN) Robustness Evaluation of a Foundation Segmentation Model Under Simulated Domain Shifts in Abdominal CT: Implications for Health Digital Twin Deployment

SAM模型在CT扫描中显示出稳定的脾脏分割能力，不受域偏移影响

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员评估了分割一切模型（SAM）在腹部CT扫描中进行脾脏分割的鲁棒性，模拟了噪声和分辨率变化等各种域偏移。研究发现，在这些模拟条件下，SAM在分割精度方面保持了稳定的性能，下降幅度极小。这些发现表明，SAM作为医学影像应用的可靠基础模型具有潜力，包括在健康数字孪生领域，因为在这些领域一致的解剖建模至关重要。 AI

影响 表明SAM在医学影像领域的可用性，可能加速健康数字孪生开发。

排序理由 学术论文，评估现有模型在特定条件下的性能。

在 arXiv cs.CV 阅读 →

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SAM模型在CT扫描中显示出稳定的脾脏分割能力，不受域偏移影响

报道来源 [2]

  1. arXiv cs.CV TIER_1 English(EN) · Sanghati Basu ·

    Robustness Evaluation of a Foundation Segmentation Model Under Simulated Domain Shifts in Abdominal CT: Implications for Health Digital Twin Deployment

    arXiv:2604.25685v1 Announce Type: cross Abstract: Foundation segmentation models such as the Segment Anything Model (SAM) have demonstrated strong generalization across natural images; however, their robustness under clinically realistic medical imaging domain shifts remains insu…

  2. arXiv cs.CV TIER_1 English(EN) · Sanghati Basu ·

    Robustness Evaluation of a Foundation Segmentation Model Under Simulated Domain Shifts in Abdominal CT: Implications for Health Digital Twin Deployment

    Foundation segmentation models such as the Segment Anything Model (SAM) have demonstrated strong generalization across natural images; however, their robustness under clinically realistic medical imaging domain shifts remains insufficiently quantified. We present a systematic sli…

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