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MS-DCANet: A Novel Segmentation Network For Multi-Modality COVID-19 Medical Images

arXiv 2210.12361 ↗First posted 22 Oct 2022eess.IV cs.CVcited by 0

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Abstract (arXiv)

The Coronavirus Disease 2019 (COVID-19) pandemic has increased the public health burden and brought profound disaster to humans. For the particularity of the COVID-19 medical images with blurred boundaries, low contrast and different infection sites, some researchers have improved the accuracy by adding more complexity. Also, they overlook the complexity of lesions, which hinder their ability to capture the relationship between segmentation sites and the background, as well as the edge contours and global context. However, increasing the computational complexity, parameters and inference speed is unfavorable for model transfer from laboratory to clinic. A perfect segmentation network needs to balance the above three factors completely. To solve the above issues, this paper propose a symmetric automatic segmentation framework named MS-DCANet. We introduce Tokenized MLP block, a novel attention scheme that use a shift-window mechanism to conditionally fuse local and global features to get more continuous boundaries and spatial positioning capabilities. It has greater understanding of irregular lesions contours. MS-DCANet also uses several Dual Channel blocks and a Res-ASPP block to improve the ability to recognize small targets. On multi-modality COVID-19 tasks, MS-DCANet achieved state-of-the-art performance compared with other baselines. It can well trade off the accuracy and complexity. To prove the strong generalization ability of our proposed model, we apply it to other tasks (ISIC 2018 and BAA) and achieve satisfactory results.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 26 of its 37 references that are in the map. First posted on arXiv 22 Oct 2022.

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References · 26

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14 more
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  2. 2020Can AI help in screening Viral and COVID-19 pneumonia?cited by 200
  3. 2022UNeXt: MLP-based Rapid Medical Image Segmentation Networkcited by 185
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  13. 2020COVID-19 Infection Map Generation and Detection from Chest X-Ray Imagescited by 28
  14. 2020MiniSeg: An Extremely Minimum Network for Efficient COVID-19 Segmentationcited by 18

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