DCELANM-Net:Medical Image Segmentation based on Dual Channel Efficient Layer Aggregation Network with Learner

TL;DR

This paper introduces DCELANM-Net, a novel medical image segmentation model that combines a dual channel efficient layer aggregation network with a micro masked autoencoder, enhancing feature learning and scalability.

Contribution

The paper presents a new architecture integrating DCELAN with Micro-MAE, improving feature extraction and model scalability for medical image segmentation.

Findings

Enhanced feature fitting through deeper network structure

Effective local feature localization via layer fusion

Scalable self-supervised learning with Micro-MAE

Abstract

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.