Chen Yehang, Chen Xiangmeng
Laboratory of Artificial Intelligence of Biomedicine, Guilin University of Aerospace Technology, Guilin, China.
School of Electronic Information and Automation, Guilin University of Aerospace Technology, Guilin, China.
Front Hum Neurosci. 2022 Oct 11;16:1019564. doi: 10.3389/fnhum.2022.1019564. eCollection 2022.
Transfer learning can improve the robustness of deep learning in the case of small samples. However, when the semantic difference between the source domain data and the target domain data is large, transfer learning easily introduces redundant features and leads to negative transfer. According the mechanism of the human brain focusing on effective features while ignoring redundant features in recognition tasks, a brain-like classification method based on adaptive feature matching dual-source domain heterogeneous transfer learning is proposed for the preoperative aided diagnosis of lung granuloma and lung adenocarcinoma for patients with solitary pulmonary solid nodule in the case of small samples. The method includes two parts: (1) feature extraction and (2) feature classification. In the feature extraction part, first, By simulating the feature selection mechanism of the human brain in the process of drawing inferences about other cases from one instance, an adaptive selected-based dual-source domain feature matching network is proposed to determine the matching weight of each pair of feature maps and each pair of convolution layers between the two source networks and the target network, respectively. These two weights can, respectively, adaptive select the features in the source network that are conducive to the learning of the target task, and the destination of feature transfer to improve the robustness of the target network. Meanwhile, a target network based on diverse branch block is proposed, which made the target network have different receptive fields and complex paths to further improve the feature expression ability of the target network. Second, the convolution kernel of the target network is used as the feature extractor to extract features. In the feature classification part, an ensemble classifier based on sparse Bayesian extreme learning machine is proposed that can automatically decide how to combine the output of base classifiers to improve the classification performance. Finally, the experimental results (the AUCs were 0.9542 and 0.9356, respectively) on the data of two center data show that this method can provide a better diagnostic reference for doctors.
迁移学习可以在小样本情况下提高深度学习的鲁棒性。然而，当源域数据与目标域数据之间的语义差异较大时，迁移学习很容易引入冗余特征并导致负迁移。根据人类大脑在识别任务中关注有效特征而忽略冗余特征的机制，针对小样本情况下孤立性肺实性结节患者的肺肉芽肿和肺腺癌术前辅助诊断，提出了一种基于自适应特征匹配双源域异构迁移学习的类脑分类方法。该方法包括两个部分：(1) 特征提取和 (2) 特征分类。在特征提取部分，首先，通过模拟人类大脑举一反三过程中的特征选择机制，提出了一种基于自适应选择的双源域特征匹配网络，分别确定两个源网络与目标网络之间每对特征图以及每对卷积层的匹配权重。这两个权重可以分别自适应地选择源网络中有利于目标任务学习的特征以及特征迁移的目的地，以提高目标网络的鲁棒性。同时，提出了一种基于多样分支块的目标网络，使目标网络具有不同的感受野和复杂路径，以进一步提高目标网络的特征表达能力。其次，将目标网络的卷积核用作特征提取器来提取特征。在特征分类部分，提出了一种基于稀疏贝叶斯极限学习机的集成分类器，它可以自动决定如何组合基分类器的输出以提高分类性能。最后，在两个中心数据上的实验结果（AUC分别为0.9542和0.9356）表明，该方法可以为医生提供更好的诊断参考。
