MFA-Net: Multi-Scale feature fusion attention network for liver tumor segmentation
<|reference_start|>MFA-Net: Multi-Scale feature fusion attention network for liver tumor segmentation: 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.<|reference_end|>
@article{yuan2024mfa-net:,
    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},
    journal={arXiv preprint arXiv:2405.04064},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.04064},
    primaryClass={cs.AI}
}