Goal: We propose a convolutional attention-based network that allows for use of pre-trained 2-D convolutional feature extractors and is extendable to multi-time-point classification in a Siamese structure. Methods: Our proposed framework is evaluated for single- and multi-time-point classification to explore the value that temporal information, such as nodule growth, adds to malignancy prediction. Results: Our results show that the proposed method outperforms a comparable 3-D network with less than half the parameters on single-time-point classification and further achieves performance gains on multi-time-point classification. Conclusions: Attention-based, Siamese 2-D pre-trained CNNs lead to fast training times and are effective for malignancy prediction from single-time-point or multiple-time-point imaging data.

中文翻译：

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使用连体卷积注意网络从纵向 CT 扫描预测肺结节恶性肿瘤

目标：我们提出了一个基于卷积注意力的网络，它允许使用预训练的二维卷积特征提取器，并且可以扩展到连体结构中的多时间点分类。方法：我们提出的框架针对单时间点和多时间点分类进行了评估，以探索时间信息（例如结节生长）增加恶性肿瘤预测的价值。结果：我们的结果表明，所提出的方法在单时间点分类的参数不到一半的情况下优于可比较的 3-D 网络，并进一步实现了多时间点分类的性能提升。结论：基于注意力的 Siamese 2-D 预训练 CNN 可缩短训练时间，并有效地从单时间点或多时间点成像数据预测恶性肿瘤。