基于注意力机制的医学影像深度哈希检索算法

Deep Hash Retrieval Algorithm for Medical Images Based on Attention Mechanism

针对现阶段医学影像检索中检索性能差、精度低、缺乏可解释性等一系列问题,提出了一种结合了注意力机制的医学影像检索算法。以深度卷积神经网络为基础,以贝叶斯模型为框架,所提算法引入了由语义特征引导的注意力机制模块,通过分类网络的引导,生成包含语义信息的局部特征描述子,同时使用全局特征与富含语义信息的局部特征作为哈希网络的输入,引导哈希网络从全局和局部的角度关注重要特征区域,增强了哈希编码的特征表达能力,并引入加权似然估计函数解决了正负样本对数量不均衡的问题。采用MAP和NDCG作为评价指标,选择ChestX-ray14数据集进行实验,将所提算法与目前常用的深度哈希方法进行对比。实验结果表明,本文算法在哈希编码不同码位下的MAP值和NDCG值都远优于现有的深度哈希方法,证明了其有效性。

A medical image retrieval method combining attention mechanism is proposed for a series of problems such as poor retrieval performance,low accuracy and lack of interpretability in current medical image retrieval.Based on deep convolutional neural networks and taking Bayesian models as the framework,the proposed algorithm introduces an attention mechanism module guided by semantic features.Local feature descriptors containing semantic information are generated under the guidance of the classification network.Both global features and local features rich in semantic information are used as inputs to the hash network,which enhances the feature representation capability of hash coding by guiding the hash network to pay attention to important feature regions from both global and local perspectives.And the weighted likelihood estimation function is introduced to solve the problem of the unbalanced number of positive and negative sample pairs.MAP and NDCG are used as evaluation metrics,and the ChestX-ray14 dataset is selected for experiments.The proposed algorithm is compared with the current commonly used deep ha-shing methods.Experiment results show that the MAP and NDCG values are much better than the existing deep hashing methods at different code levels of hash coding,which proves the effectiveness of the proposed algorithm.