基于迁移学习的多视角乳腺肿块和钙化簇分类方法

针对乳腺肿块和钙化簇分类任务中可用训练数据量较少的问题,结合乳腺钼靶图成像特点提出了一种基于二次迁移学习的多视角模型。首先,使用CBIS-DDSM制作乳腺局部组织切片数据集来预训练主干网络,完成主干网络的领域适应性学习,使之具备基本的病理特征捕捉能力;随后,把主干网络二次迁移到多视角网络中,在绵阳市中心医院数据集上进行微调,同时利用CBIS-DDSM增加训练的正样本数量以提升网络的泛化能力。实验结果表明,领域适应性学习和数据扩充策略平均提升了17%性能指标,取得了94%和90%的肿块和钙化簇曲线下面积(AUC)值。

Mass and calcification classification method in mammogram based on multi-view transfer learning

In order to solve the problem of insufficient available training data in the classification task of breast mass and calcification, a multi-view model based on secondary transfer learning was proposed combining with imaging characteristics of mammogram. Firstly, CBIS-DDSM (Curated Breast Imaging Subset of Digital Database for Screening Mammography) was used to construct the breast local tissue section dataset for the pre-training of the backbone network, and the domain adaptation learning of the backbone network was completed, so the backbone network had the essential ability of capturing pathological features. Then, the backbone network was secondarily transferred to the multi-view model and was fine-tuned based on the dataset of Mianyang Central Hospital. At the same time, the number of positive samples in the training was increased by CBIS-DDSM to improve the generalization ability of the network. The experimental results show that the domain adaption learning and data augmentation strategy improves the performance criteria by 17% averagely and achieves 94% and 90% AUC (Area Under Curve) values for mass and calcification respectively.