基于自适应多分辨率特征学习的CNV分型网络

Adaptive multi-resolution feature learning network for CNV classification

为解决不同脉络膜新生血管(choroidal neovascularization,CNV)类型间较小区分性带来的分型难度和光学相干断层扫描(optical coherence tomography,OCT)图像中噪声对分型精度的影响,提出自适应多分辨率特征学习的CNV分型方法,其包含多分辨率特征学习和自适应特征选择模块。在多分辨率特征学习模块中,融合具有不同类型CNV细节信息的底层特征和具有语义信息的高层特征,同时引入渐进式的训练方式增强特征表示能力。在自适应特征选择模块中,通过引入注意力机制,对最后分型起关键作用的特征进行增强,进一步提升特征的区分性。在自建的CNV数据集上进行试验,试验结果表明,评价指标上的测试评分分别为91.3%、86.6%、89.2%和90.6%。提出的自适应多分辨率特征学习的CNV分型方法优于现有的其他分类方法。

The large similarity between different choroidal neovascularization(CNV) types increased the difficulty of classification and the noise in optical coherence tomography(OCT) images affected the classification accuracy.To address the issues,an adaptive multi-resolution feature learning CNV classification network(AMFL-net) was proposed,which consisted of multi-resolution feature learning and adaptive feature selection modules.In the multi-resolution feature learning module,the low-level vision features and the high-level semantic features were fused.The progressive training method was introduced to enhance the feature representation ability.In the adaptive feature selection module,the attention mechanism was introduced to enhance the features that played a key role in the final classification,and further improved the discrimination ability of features.Experimental results showed that the test scores on the evaluation indicators were 91.3%,86.6%,89.2% and 90.6% respectively,and the proposed AMFL-net outperformed traditional classification methods.