  Purpose: In recent years, Non-Local based methods have been successfully
applied to lung nodule classification. However, these methods offer 2D
attention or limited 3D attention to low-resolution feature maps. Moreover,
they still depend on a convenient local filter such as convolution as full 3D
attention is expensive to compute and requires a big dataset, which might not
be available.
  Methods: We propose to use 3D Axial-Attention, which requires a fraction of
the computing power of a regular Non-Local network (i.e., self-attention).
Unlike a regular Non-Local network, the 3D Axial-Attention network applies the
attention operation to each axis separately. Additionally, we solve the
invariant position problem of the Non-Local network by proposing to add 3D
positional encoding to shared embeddings.
  Results: We validated the proposed method on 442 benign nodules and 406
malignant nodules, extracted from the public LIDC-IDRI dataset by following a
rigorous experimental setup using only nodules annotated by at least three
radiologists. Our results show that the 3D Axial-Attention model achieves
state-of-the-art performance on all evaluation metrics, including AUC and
Accuracy.
  Conclusions: The proposed model provides full 3D attention, whereby every
element (i.e., pixel) in the 3D volume space attends to every other element in
the nodule effectively. Thus, the 3D Axial-Attention network can be used in all
layers without the need for local filters. The experimental results show the
importance of full 3D attention for classifying lung nodules.
