Medical Image Segmentation using Squeeze-and-Expansion Transformers

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Medical Image Segmentation using Squeeze-and-Expansion Transformers
Title:
Medical Image Segmentation using Squeeze-and-Expansion Transformers
Journal Title:
Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence
Keywords:
Publication Date:
11 August 2021
Citation:
Li, S., Sui, X., Luo, X., Xu, X., Liu, Y., & Goh, R. (2021). Medical Image Segmentation using Squeeze-and-Expansion Transformers. Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence. https://doi.org/10.24963/ijcai.2021/112
Abstract:
Medical image segmentation is important for computer-aided diagnosis. Good segmentation demands the model to see the big picture and fine details simultaneously, i.e., to learn image features that incorporate large context while keep high spatial resolutions. To approach this goal, the most widely used methods -- U-Net and variants, extract and fuse multi-scale features. However, the fused features still have small "effective receptive fields" with a focus on local image cues, limiting their performance. In this work, we propose Segtran, an alternative segmentation framework based on transformers, which have unlimited "effective receptive fields" even at high feature resolutions. The core of Segtran is a novel Squeeze-and-Expansion transformer: a squeezed attention block regularizes the self attention of transformers, and an expansion block learns diversified representations. Additionally, we propose a new positional encoding scheme for transformers, imposing a continuity inductive bias for images. Experiments were performed on 2D and 3D medical image segmentation tasks: optic disc/cup segmentation in fundus images (REFUGE'20 challenge), polyp segmentation in colonoscopy images, and brain tumor segmentation in MRI scans (BraTS'19 challenge). Compared with representative existing methods, Segtran consistently achieved the highest segmentation accuracy, and exhibited good cross-domain generalization capabilities.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the A*STAR - Career Development Fund
Grant Reference no. : C210112016

This research / project is supported by the A*STAR - Advanced Manufacturing and Engineering (AME) programme
Grant Reference no. : A18A2b0046
Description:
ISBN:
978-0-9992411-9-6
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