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English(EN) GD-MIL: Grade-Disentangled Multiple Instance Learning for Multimodal Biochemical Recurrence Prediction in Prostate Cancer

新的GD-MIL方法使用H&E图像预测前列腺癌复发

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了一种名为梯度解耦多实例学习（GD-MIL）的新方法，以改进前列腺癌生化复发的预测。该方法使用全切片图像（WSIs）提取超越传统Gleason评分的预后信息，而Gleason评分是当前风险分层的一个重大限制。GD-MIL达到了0.704的C指数，优于临床基线和现有的仅影像模型，表明H&E形态学具有有价值的补充预后数据。 AI

影响 这项研究可能带来更准确的前列腺癌复发预测，从而改善患者分层和治疗决策。

排序理由 该集群包含一篇详细介绍一种新医疗预测方法的论文。

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 →

新的GD-MIL方法使用H&E图像预测前列腺癌复发

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该集群包含一篇详细介绍一种新医疗预测方法的论文。
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报道来源 [2]

  1. arXiv cs.CV TIER_1 English(EN) · Dasari Naga Raju ·

    GD-MIL：前列腺癌多模态生化复发预测的等级解耦多实例学习

    arXiv:2606.09453v1 Announce Type: new Abstract: Biochemical recurrence (BCR) after radical prostatectomy is a critical endpoint in prostate cancer, yet risk stratification relies almost entirely on variables dominated by Gleason grade. Whether H&amp;E whole slide images (WSIs) ca…

  2. arXiv cs.CV TIER_1 English(EN) · Dasari Naga Raju ·

    GD-MIL：前列腺癌多模态生化复发预测的等级解耦多实例学习

    Biochemical recurrence (BCR) after radical prostatectomy is a critical endpoint in prostate cancer, yet risk stratification relies almost entirely on variables dominated by Gleason grade. Whether H&E whole slide images (WSIs) carry prognostic signal beyond grade, and whether mult…

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