DmADs-Net: Dense multiscale attention and depth-supervised network for medical image segmentation
<|reference_start|>DmADs-Net: Dense multiscale attention and depth-supervised network for medical image segmentation: Deep learning has made important contributions to the development of medical image segmentation. Convolutional neural networks, as a crucial branch, have attracted strong attention from researchers. Through the tireless efforts of numerous researchers, convolutional neural networks have yielded numerous outstanding algorithms for processing medical images. The ideas and architectures of these algorithms have also provided important inspiration for the development of later technologies.Through extensive experimentation, we have found that currently mainstream deep learning algorithms are not always able to achieve ideal results when processing complex datasets and different types of datasets. These networks still have room for improvement in lesion localization and feature extraction. Therefore, we have created the Dense Multiscale Attention and Depth-Supervised Network (DmADs-Net).We use ResNet for feature extraction at different depths and create a Multi-scale Convolutional Feature Attention Block to improve the network's attention to weak feature information. The Local Feature Attention Block is created to enable enhanced local feature attention for high-level semantic information. In addition, in the feature fusion phase, a Feature Refinement and Fusion Block is created to enhance the fusion of different semantic information.We validated the performance of the network using five datasets of varying sizes and types. Results from comparative experiments show that DmADs-Net outperformed mainstream networks. Ablation experiments further demonstrated the effectiveness of the created modules and the rationality of the network architecture.<|reference_end|>
@article{fu2024dmads-net:,
    title={DmADs-Net: Dense multiscale attention and depth-supervised network for
  medical image segmentation},
    author={Zhaojin Fu, Zheng Chen, Jinjiang Li, Lu Ren},
    journal={arXiv preprint arXiv:2405.00472},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.00472},
    primaryClass={eess.IV cs.CV}
}