计算机工程与应用 ›› 2024, Vol. 60 ›› Issue (7): 26-40.DOI: 10.3778/j.issn.1002-8331.2305-0030
姜良,张程,魏德健,曹慧,杜昱峥
出版日期:
2024-04-01
发布日期:
2024-04-01
JIANG Liang, ZHANG Cheng, WEI Dejian, CAO Hui, DU Yuzheng
Online:
2024-04-01
Published:
2024-04-01
摘要: 骨质疏松症是一种由于骨密度下降引起骨折危险性增加的全身性疾病,临床上以影像学检查作为诊断依据。近几年深度学习方法在骨骼医学图像处理领域取得突破性进展,针对骨质疏松辅助诊断中所采用的深度学习方法进行了梳理总结。介绍了常用的影像学数据集,系统阐述了卷积神经网络、循环神经网络、深度置信网络、生成对抗网络在骨质疏松分类中的应用,阐述了全卷积网络、U-Net在骨质疏松病灶区域分割中的应用,同时介绍了最新AI模型ChatGPT的潜在应用,比较不同模型的性能,指出该领域目前存在的难点并提出相应的展望。
姜良, 张程, 魏德健, 曹慧, 杜昱峥. 深度学习在骨质疏松辅助诊断中的应用[J]. 计算机工程与应用, 2024, 60(7): 26-40.
JIANG Liang, ZHANG Cheng, WEI Dejian, CAO Hui, DU Yuzheng. Deep Learning in Aided Diagnosis of Osteoporosis[J]. Computer Engineering and Applications, 2024, 60(7): 26-40.
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