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Updated: May 5, 2026

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
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通过生成对抗网络进行合成生殖尿图像合成:增强人工智能诊断精度

Derek J Van Booven1, Cheng-Bang Chen2, Sheetal Malpani3

  • 1John P Hussman Institute for Human Genomics, Miller School of Medicine, University of Miami, Miami, FL 33136, USA.

Journal of personalized medicine
|July 27, 2024
PubMed
概括

生成对抗网络 (GAN) 创建现实的合成生殖尿组织图像,克服计算病理学的数据稀缺性. 这种数据增强增强了诊断模型的性能,以更好地检测疾病.

关键词:
没有了,没有了,没有了.诊断 诊断 诊断 诊断 诊断数字成像数字成像机器学习是机器学习.病理学的病理学预测 预测 预测 预测前列腺癌是前列腺癌.

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