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MFA-Net: Multi-Scale feature fusion attention network for liver tumor segmentation

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Yanli Yuan,Bingbing Wang,Chuan Zhang,Jingyi Xu,Ximeng Liu,Liehuang Zhu
cs.AI
2 citations published

Abstract

Segmentation of organs of interest in medical CT images is beneficial for diagnosis of diseases. Though recent methods based on Fully Convolutional Neural Networks (F-CNNs) have shown success in many segmentation tasks, fusing features from images with different scales is still a challenge: (1) Due to the lack of spatial awareness, F-CNNs share the same weights at different spatial locations. (2) F-CNNs can only obtain surrounding information through local receptive fields. To address the above challenge, we propose a new segmentation framework based on attention mechanisms, named MFA-Net (Multi-Scale Feature Fusion Attention Network). The proposed framework can learn more meaningful feature maps among multiple scales and result in more accurate automatic segmentation. We compare our proposed MFA-Net with SOTA methods on two 2D liver CT datasets. The experimental results show that our MFA-Net produces more precise segmentation on images with different scales.

Cite this paper

@article{yuan2024,
  title = {MFA-Net: Multi-Scale feature fusion attention network for liver tumor segmentation},
  author = {Yanli Yuan and Bingbing Wang and Chuan Zhang and Jingyi Xu and Ximeng Liu and Liehuang Zhu},
  year = {2024},
  month = {May},
  doi = {10.48550/arxiv.2405.04064},
  url = {https://doi.org/10.48550/arxiv.2405.04064},
}