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English(EN) Order-Aware 2.5D Multiple Instance Learning for Preoperative MRI-Based Perineural Invasion Risk Assessment in Intrahepatic Cholangiocarcinoma

新的OAS-MIL框架通过MRI扫描预测癌症风险 · 已追踪2个来源

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了一个名为Order-Aware Slab Multiple Instance Learning (OAS-MIL) 的弱监督框架，用于使用术前MRI预测肝内胆管癌 (ICC) 的神经侵袭 (PNI) 风险。该方法将MRI数据表示为2.5D切片的有序序列，无需切片或体素级注释即可进行患者级别的PNI预测。在五折交叉验证中，OAS-MIL达到了0.770的平均AUROC，优于现有的体积和MIL基线，表明轴向顺序对于此类预测是有价值的归纳偏置。 AI

影响 该框架可以改善癌症扩散的早期检测，可能带来更好的患者预后和治疗计划。

排序理由 该集群描述了一篇详细介绍用于医学影像分析的新型机器学习框架的新研究论文。

在 Hugging Face Daily Papers 阅读 →

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新的OAS-MIL框架通过MRI扫描预测癌症风险 · 已追踪2个来源

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该集群描述了一篇详细介绍用于医学影像分析的新型机器学习框架的新研究论文。
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报道来源 [2]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    用于肝内胆管癌术前基于MRI的神经周围浸润风险评估的面向订单的2.5D多实例学习

    Perineural invasion (PNI) is an adverse histopathologic marker in intrahepatic cholangiocarcinoma (ICC), but it is usually confirmed only after resection. Preoperative T2-weighted MRI may provide noninvasive imaging cues predictive of PNI, although labels are available only at th…

  2. arXiv cs.CV TIER_1 English(EN) · Hyunsu Go, Youngung Han, Kyeonghun Kim, Jinyong Jun, Junbeom Lee, Dohyun Kweon, Yului Jeong, Suah Park, Sungha Park, Anna Jung, Woo Kyoung Jeong, Ken Ying-Kai Liao, Hyuk-Jae Lee, Nam-Joon Kim ·

    用于肝内胆管癌术前基于MRI的神经周围浸润风险评估的订单感知2.5D多实例学习

    arXiv:2609.11271v1 Announce Type: new Abstract: Perineural invasion (PNI) is an adverse histopathologic marker in intrahepatic cholangiocarcinoma (ICC), but it is usually confirmed only after resection. Preoperative T2-weighted MRI may provide noninvasive imaging cues predictive …

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