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模拟胸部放射图的临床特征,用于医学图像探索和CNN可解释性,使用基于风格的生成对抗自编码器

Kyle A Hasenstab1,2, Lewis Hahn3, Nick Chao4

  • 1Department of Mathematics and Statistics, San Diego State University, 5500 Campanile Drive, San Diego, CA, 92182, USA. kylehasenstab@gmail.com.

Scientific reports
|October 18, 2024
PubMed
概括

我们开发了SEE-GAAN,这是一个新的AI可解释性框架,用于医学成像. 它可视化了AI模型如何解释特征,提高对临床AI应用程序的理解和信任.

关键词:
自动编码器自动编码器胸部X射线成像 胸部X射线成像卷积神经网络是一种卷积神经网络.可解释的人工智能生成性的对抗性网络.

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