Memory-Augmented Capsule Network for Adaptable Lung Nodule Classification
Memory-Augmented Capsule Network for Adaptable Lung Nodule Classification
复制标题
用于自适应肺结节分类的记忆增强胶囊网络
DOI：
10.1109/tmi.2021.3051089
复制
发表时间：
2021-10-01
影响因子：
10.6
通讯作者：
Nguyen, Hien V.
中科院分区：
文献类型：
--
作者：
Mobiny, Aryan;Yuan, Pengyu;Nguyen, Hien V.
计算机辅助诊断（CAD）系统必须不断科普由不同的传感技术、成像协议和患者群体引起的数据分布的永久变化。使这些系统适应新的领域通常需要大量的标记数据进行重新训练。这一过程是劳动密集型和耗时的。我们提出了一个记忆增强胶囊网络的CAD模型快速适应新的领域。它由一个胶囊网络和一个记忆增强任务网络组成，前者用于从一些高维输入中提取特征嵌入，后者用于从目标域中利用其存储的知识。我们的网络能够有效地适应看不见的领域，只使用一些注释的样本。我们使用大规模的公共肺结节数据集（LUNA），加上我们自己收集的肺结节和附带的肺结节数据集来评估我们的方法。当在LUNA数据集上训练时，我们的网络只需要从我们收集的肺结节和偶发肺结节数据集中额外采集30个样本，就可以实现临床相关性能（分别为0.925和0.891的接收工作特征曲线（AUROC）下面积）。这个结果相当于使用少两个数量级的标记训练数据，同时实现相同的性能。我们通过引入重噪声、伪影和对抗性攻击来进一步评估我们的方法。在这些恶劣的条件下，我们的网络的AUROC保持在0.7以上，而最先进的方法的性能降低到机会水平。
Computer-aided diagnosis (CAD) systems must constantly cope with the perpetual changes in data distribution caused by different sensing technologies, imaging protocols, and patient populations. Adapting these systems to new domains often requires significant amounts of labeled data for re-training. This process is labor-intensive and time-consuming. We propose a memory-augmented capsule network for the rapid adaptation of CAD models to new domains. It consists of a capsule network that is meant to extract feature embeddings from some high-dimensional input, and a memory-augmented task network meant to exploit its stored knowledge from the target domains. Our network is able to efficiently adapt to unseen domains using only a few annotated samples. We evaluate our method using a large-scale public lung nodule dataset (LUNA), coupled with our own collected lung nodules and incidental lung nodules datasets. When trained on the LUNA dataset, our network requires only 30 additional samples from our collected lung nodule and incidental lung nodule datasets to achieve clinically relevant performance (0.925 and 0.891 area under receiving operating characteristic curves (AUROC), respectively). This result is equivalent to using two orders of magnitude less labeled training data while achieving the same performance. We further evaluate our method by introducing heavy noise, artifacts, and adversarial attacks. Under these severe conditions, our network's AUROC remains above 0.7 while the performance of state-of-the-art approaches reduce to chance level.