DCELANM-Net:Medical Image Segmentation based on Dual Channel Efficient Layer Aggregation Network with Learner
This work addresses medical image segmentation, an incremental improvement in a domain-specific area.
The authors tackled medical image segmentation by proposing DCELANM-Net, which combines a Dual Channel Efficient Layer Aggregation Network (DCELAN) for improved feature learning and a Micro Masked Autoencoder (Micro-MAE) for self-supervised scalability, achieving more accurate localization of local features.
The DCELANM-Net structure, which this article offers, is a model that ingeniously combines a Dual Channel Efficient Layer Aggregation Network (DCELAN) and a Micro Masked Autoencoder (Micro-MAE). On the one hand, for the DCELAN, the features are more effectively fitted by deepening the network structure; the deeper network can successfully learn and fuse the features, which can more accurately locate the local feature information; and the utilization of each layer of channels is more effectively improved by widening the network structure and residual connections. We adopted Micro-MAE as the learner of the model. In addition to being straightforward in its methodology, it also offers a self-supervised learning method, which has the benefit of being incredibly scaleable for the model.