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Updated: Jun 9, 2026

Automated Quantification of Hematopoietic Cell – Stromal Cell Interactions in Histological Images of Undecalcified Bone
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使用基于扩散的瓦斯斯坦生成对抗网络的临床应用,用于血液细胞的形态分析

Hyun-Young Kim1, Emmanuel Edward Ngasa2, Hee-Jin Kim1

  • 1Department of Laboratory Medicine and Genetics, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.

Journal of clinical laboratory analysis
|October 6, 2025
PubMed
概括

基于扩散的Wasserstein生成对抗网络与梯度惩罚 (DWGAN-GP) 显著改善了血细胞分类的准确性. 这种人工智能方法通过解决数据不平衡和提高准确性来增强血液学疾病的诊断,特别是对于关键细胞类型.

关键词:
血液细胞 细胞 血细胞这是分类分类的分类.深度学习是一种深度学习.扩散模型的扩散模型.生成性的对抗性网络.血液学疾病 血液学疾病

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