  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.
