计算机科学 ›› 2024, Vol. 51 ›› Issue (2): 135-141.doi: 10.11896/jsjkx.221100260
丁天舒, 陈媛媛
DING Tianshu, CHEN Yuanyuan
摘要： 更精细化的糖尿病性视网膜病变眼底图像分割结果,可以更好地辅助医生进行诊断。大规模高分辨率的分割数据集的出现,为更精细化的分割提供了有利条件。基于U-Net的主流分割网络,使用基于局部运算的卷积操作进行像素预测时无法充分挖掘全局信息,网络模型采用单输入单输出的结构,难以获取多尺度特征信息。为了最大程度地利用现有的大规模高分辨率的眼底图像病灶分割数据集,实现更精细化的分割,需要设计更好的分割方法。文中基于自注意力机制和多尺度输入输出结构对U-Net进行改造,提出了一种新的分割网络SAM-Net,用自注意力模块代替传统卷积模块,增大网络获取全局信息的能力,引入多尺度输入和多尺度输出结构,使网络更容易获取多尺度特征信息。使用图片切片方法来缩小模型的输入尺寸,防止神经网络模型因为输入图片像素过大而导致训练难度增大。最终在IDRiD数据集和FGADR数据集上进行实验,结果表明,SAM-Net可以达到比其他方法更优的性能。
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