计算机科学 ›› 2022, Vol. 49 ›› Issue (6A): 60-65.doi: 10.11896/jsjkx.201200072
杜丽君1, 唐玺璐2, 周娇2, 陈玉兰2, 程建2
DU Li-jun1, TANG Xi-lu2, ZHOU Jiao2, CHEN Yu-lan2, CHENG Jian2
摘要： 利用深度学习实现阿尔茨海默症分类已成为近年来医学图像研究热点之一。为了解决现有模型难以有效提取医学图像特征及疾病分类辅助信息资源浪费等问题,基于深度三维卷积神经网络提出一种引入注意力机制和多任务学习的阿尔茨海默症分类方法。首先,利用改进的基础C3D网络,生成较粗糙的低级特征图;然后,将其分别输入至引入注意力机制的卷积块与普通卷积块中,前者关注MRI图像的结构特性,能获取特征图中不同像素位置特有的注意力权重,与后者输出的特征图对应相乘;最后,利用不同的全连接层来实现多任务学习,获得包含主分类任务在内的3种输出,另2种输出在训练过程中通过反向传播优化主分类任务,得到优化后的阿尔茨海默症分类结果。实验结果表明,与目前已有阿尔茨海默症分类方法相比,所提方法在ADNI数据集上的分类准确率等指标均有所提升,有助于推进后续疾病分类研究。
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