计算机科学 ›› 2023, Vol. 50 ›› Issue (6A): 220400273-6.doi: 10.11896/jsjkx.220400273
杨靖怡1, 李芳1,2, 康晓东1, 王笑天1, 刘汉卿1, 韩俊玲1
YANG Jingyi1, LI Fang1,2, KANG Xiaodong1, WANG Xiaotian1, LIU Hanqing1, HAN Junling1
摘要: 超声影像分割既是医学影像图像处理的重要环节,也是临床诊断的常用技术手段。文中提出将SegFormer网络模型用于实现医学超声影像图像的精准分割。一方面,将超声标签图转化为单通道形式,并对其进行二值化处理,以完成对数据集图像的预处理;另一方面,采用迁移学习的方式载入预训练模型,用于微调已经训练好的模型参数,并选用带有动量的随机梯度下降优化器来加速收敛速度及减小震荡。与FCN,UNet和DeepLabV3的对比实验结果表明,该模型在乳腺结节超声影像数据集上的各项评估指标均为最优,mIoU,Acc,DSC和Kappa分别为81.32%,96.22%,88.91%和77.85%。实验结果还表明,该模型在不同超声影像数据集中表现出了良好的鲁棒性。
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