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International Journal of Computer Assisted Radiology and Surgery

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31-05-2021 | Short communication

3D axial-attention for lung nodule classification

Authors: Mundher Al-Shabi, Kelvin Shak, Maxine Tan

Published in: International Journal of Computer Assisted Radiology and Surgery | Issue 8/2021

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Abstract

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.

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Title: 3D axial-attention for lung nodule classification
Authors: Mundher Al-Shabi
Kelvin Shak
Maxine Tan
Publication date: 31-05-2021
Publisher: Springer International Publishing
Published in: International Journal of Computer Assisted Radiology and Surgery / Issue 8/2021
Print ISSN: 1861-6410
Electronic ISSN: 1861-6429
DOI: https://doi.org/10.1007/s11548-021-02415-z

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Abstract

Purpose

Methods

Results

Conclusions

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