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Updated: Jul 19, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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空间Omics驱动的交叉训练 应用到基于图形的深度学习用于癌症病理学分析

Zarif Azher, Michael Fatemi, Yunrui Lu

    bioRxiv : the preprint server for biology
    |August 14, 2023
    PubMed
    概括

    将空间转录学与深度学习相结合,可以增强癌症组织病理学分析. 这种方法通过结合分子和成像数据来改善癌症分期,转移和生存的预测.

    科学领域:

    • 计算病理学计算病理学
    • 分子病理学分子病理学
    • 医学中的人工智能

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    背景情况:

    • 基于图形的深度学习在癌症组织病理学中表现出色,通过分析形态学和结构来预测结果.
    • 现有的方法依赖于图像补丁嵌入作为幻灯片图中的节点属性.
    • 空间奥米克,就像空间转录学一样,提供了详细的分子信息.

    结论:

    • 空间奥米克斯数据挖掘显著增强了病理学工作流程的深度学习.
    • 结合分子和组织学数据,可以更全面地了解致癌的发生.
    • 这种方法有望提高癌症研究中的诊断和预后准确性.