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English(EN) Robustifying pathology foundation models via fine-tuning

新方法提高病理基础模型鲁棒性 · 跟踪2个来源

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了新方法来提高病理学基础模型的鲁棒性。一种方法CRoMa测量样本级异质性和分布鲁棒性，为选择即使在组织制备和扫描存在差异时也能表现良好的模型提供了一种途径。另一种方法涉及一种微调方案，该方案在不牺牲性能的情况下，持续提高各种病理基础模型的鲁棒性和下游性能，这在PathoROB、HEST和THUNDER等基准测试中得到了证明。 AI

影响 这些进展可能带来更可靠、更具泛化性的医疗诊断AI工具，从而改善患者的治疗效果。

排序理由 两篇在arXiv上发表的学术论文介绍了用于改进病理学基础模型的新方法。

在 arXiv cs.AI 阅读 →

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新方法提高病理基础模型鲁棒性 · 跟踪2个来源

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两篇在arXiv上发表的学术论文介绍了用于改进病理学基础模型的新方法。
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报道来源 [2]

  1. arXiv cs.AI TIER_1 English(EN) · Cl\'ement Grisi, Jeroen van der Laak, Geert Litjens ·

    超越计数：病理基础模型的分布鲁棒性裕度

    arXiv:2607.25497v1 Announce Type: cross Abstract: Pathology foundation models are approaching clinical deployment, yet remain vulnerable to systematic non-biological variation across centres. Differences in tissue preparation, staining and scanning are strongly encoded in their r…

  2. arXiv cs.AI TIER_1 English(EN) · Alexandre Filiot, Oskar Thaeter, Benoit Schmauch, Lionel Guillou ·

    通过微调增强病理基础模型

    arXiv:2607.22861v1 Announce Type: cross Abstract: Pathology foundation models (FMs) produce powerful tile-level representations which remain sensitive to scanner and staining variability, undermining deployment across laboratories. We develop a novel fine-tuning recipe that impro…

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