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We propose a convolutional attentionbased network that allows for use of pre-trained 2-D convolutional feature extractors and is extendable to multitime-point classification in a Siamese structure. Methods: Our proposed framework is evaluated for single-and multitime-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.

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Lung cancer is the leading cause of cancer-related death among both men and women and second most commonly diagnosed cancer, accounting for 18% of the total cancer deaths world-wide [4]. Screening high-risk patients with low dose Computed Tomography (CT) can lead to earlier treatment and increase the survival rate [5]. However, cancer diagnosis remains a challenging problem due to the subtle visual differences between benign and malignant nodules in CT images. Hence, computer-aided diagnosis (CADx) systems may prove useful in assisting radiologists in the malignancy prediction task. Previously we developed a convolutional attention-based network that allows for use of pre-trained 2-D convolutional feature extractors and is extendable to multi-timepoint classification in a Siamese structure [6]. The use of a sequence of parallel 2-D CNNs in place of a 3D CNN will result in significant reduction in the number of network parameters. In this paper, we keep the same overall structure utilized in [6] including the attention mechanism. However, herein, we report on use of Efficient-Net [1] for 2-D feature extractors due to its success on the Image-Net classification challenge. Variations of the Efficient-Net B0 to B7 pretrained on Image-Net were fine-tuned and applied to NLSTx data [6] a subset of data acquired in the National Lung Screening Trial (NLST) [2]. NLSTx includes data from biopsy confirmed scans in 650 benign and 207 malignant nodules at up to 3 time points. In our study, the performance of the best Efficient-Net reached an area under ROC curve of .7896 for the benign/malignant classification.