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    <title>Jian Gong | ViLab</title>
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    <description>Jian Gong</description>
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      <title>Jian Gong</title>
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      <title>Advancing presurgical non-invasive molecular subgroup prediction in medulloblastoma using artificial intelligence and MRI signatures</title>
      <link>https://vilab.team/publication/advancing-presurgical-non-invasive-molecular-subgroup-predic/</link>
      <pubDate>Mon, 08 Jul 2024 00:00:00 +0000</pubDate>
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      <description>&lt;p&gt;本文构建了涵盖中国和美国13个中心934例髓母细胞瘤患者的国际分子特征数据库，利用人工智能和MRI影像特征实现术前无创的分子亚型预测。通过交叉验证、外部验证和连续验证，证明了模型作为通用分子诊断分类器的有效性，并通过对MRI特征的详细分析，从影像学角度深化了对髓母细胞瘤的理解，为临床管理提供了低成本、可推广的替代路径。&lt;/p&gt;
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      <title>Tmformer: Token merging transformer for brain tumor segmentation with missing modalities</title>
      <link>https://vilab.team/publication/tmformer-token-merging-transformer-for-brain-tumor-segmentat/</link>
      <pubDate>Sun, 24 Mar 2024 00:00:00 +0000</pubDate>
      <guid>https://vilab.team/publication/tmformer-token-merging-transformer-for-brain-tumor-segmentat/</guid>
      <description>&lt;p&gt;本文提出 TMFormer，一种用于缺失模态脑肿瘤分割的 Token 合并 Transformer。该方法通过提取并合并可用模态为更紧凑的 token 序列，解决现有方法以零图填充缺失模态带来的特征偏差与冗余计算问题。其核心包括单模态 Token 合并块（UMB）和多模态 Token 合并块（MMB），分别增强单模态表示并缓解多模态融合偏差。在 BraTS 2018 和 2020 数据集上的实验表明，TMFormer 在缺失模态场景下优于现有方法。&lt;/p&gt;
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