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English(EN) Beyond Classification: Pathology Foundation Models as Detection Encoders for Mitotic Figures

病理学基础模型在有丝分裂计数检测方面展现潜力

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员探索了病理学基础模型（FMs）作为有丝分裂计数检测编码器的有效性，将其应用从通常的分类任务扩展开来。该研究将几种基础模型，包括H-optimus-0和Virchow模型，与ResNet50基线进行了比较，并将它们集成到不同的检测架构中，如RetinaNet、Faster R-CNN和Deformable DETR。结果表明，H-optimus-0和Virchow模型表现出具有竞争力的性能，这表明通过图像级自监督训练的基础模型潜在空间适合直接进行有丝分裂计数检测，并且可能在域外数据集上提供更强的鲁棒性。 AI

影响 展示了基础模型在医学影像中除分类外的专用检测任务的潜力。

排序理由 关于病理学检测基础模型的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

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病理学基础模型在有丝分裂计数检测方面展现潜力

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报道来源 [1]

  1. arXiv cs.CV TIER_1 English(EN) · Sweta Banerjee, Alireza Teimoury, Nils Porsche, Alexandra K. Stoll, Viktoria Weiss, Niklas Hargarter, Jonas Ammeling, Thomas Conrad, Christoph Stroblberger, Christopher Kaltnecker, Robert Klopfleisch, Christof A. Bertram, Katharina Breininger, Marc Aubre… ·

    超越分类：病理学基础模型作为有丝分裂计数检测编码器

    arXiv:2607.28007v1 Announce Type: new Abstract: Pathology foundation models (FMs) are models trained on vast amounts of typically unlabeled data and have been shown to yield regularized latent spaces that can be used effectively in downstream classification tasks. This is also tr…

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