[用于口腔粘膜病变细分的规模不变功能增强的深度学习框架]
1Center of Information, Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine & Clinical Research Center for Oral Diseases of Zhejiang Province & Key Laboratory of Oral Biomedical Research of Zhejiang Province & Cancer Center of Zhejiang University & Engineering Research Center of Oral Biomaterials and Devices of Zhejiang Province, Hangzhou 310005, China.
概括
新的PixelSIFT-UNet模型通过将深度学习与规模不变特征转换 (SIFT) 算法集成,显著提高了口腔粘膜病变细分的准确性. 这种人工智能方法为诊断口腔平和白血病等疾病提供了更高的精度.
科学领域:
- 医学成像分析 医学成像分析
- 医疗保健中的人工智能
- 计算病理学计算病理学


