mlsanityData to 7 Oct 2026

Quadruple Augmented Pyramid Network for Multi-class COVID-19 Segmentation via CT

arXiv 2103.05546 ↗First posted 9 Mar 2021eess.IV cs.CVcited by 3

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Abstract (arXiv)

COVID-19, a new strain of coronavirus disease, has been one of the most serious and infectious disease in the world. Chest CT is essential in prognostication, diagnosing this disease, and assessing the complication. In this paper, a multi-class COVID-19 CT segmentation is proposed aiming at helping radiologists estimate the extent of effected lung volume. We utilized four augmented pyramid networks on an encoder-decoder segmentation framework. Quadruple Augmented Pyramid Network (QAP-Net) not only enable CNN capture features from variation size of CT images, but also act as spatial interconnections and down-sampling to transfer sufficient feature information for semantic segmentation. Experimental results achieve competitive performance in segmentation with the Dice of 0.8163, which outperforms other state-of-the-art methods, demonstrating the proposed framework can segments of consolidation as well as glass, ground area via COVID-19 chest CT efficiently and accurately.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 18 of its 34 references that are in the map. First posted on arXiv 9 Mar 2021.

Builds on

18 of 34 references.

References · 18

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6 more
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Citing papers per year: 2022: 2; 2023: 0; 2024: 1.

3 papers.

  1. 2024Semi-Mamba-UNet: Pixel-Level Contrastive and Pixel-Level Cross-Supervised Visual Mamba-based UNet for Semi-Supervised Medical Image Segmentationcited by 20
  2. 2022When CNN Meet with ViT: Towards Semi-Supervised Learning for Multi-Class Medical Image Semantic Segmentationcited by 13
  3. 2022WSC-Trans: A 3D network model for automatic multi-structural segmentation of temporal bone CTcited by 0