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Updated: Jun 13, 2026

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因果X-Net:一种因果导向的可解释的脑瘤细分网络

P Suman Prakash1, Patike Kiran Rao2, M Jahir Pasha3

  • 1Department of Computer Science and Engineering-Artificial Intelligence, G Pullaiah College of Engineering and Technology, Kurnool, Andra Pradesh, India.

Frontiers in medicine
|November 10, 2025
PubMed
概括

新的人工智能模型CausalX-Net通过使用因果推断来提高MRI扫描的脑瘤细分,以获得更好的准确性和可解释性. 这种方法为放射科医生提供了可操作的见解,增强了临床环境中的人工智能辅助神经成像.

关键词:
因果X-Net是因为因果X-Net.大脑瘤的细分 脑瘤的细分因果关系效应 (CE) 地图.反事实的解释反事实的解释.深度学习是一种深度学习.可解释的人工智能 (XAI)

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Protein Networks02:26

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...