SRP&PASMLP‐Net: Lightweight skin lesion segmentation network based on structural re‐parameterization and parallel axial shift multilayer perceptron

Abstract

AbstractAccurate skin lesion segmentation (SLS) plays an essential role in the computer‐aided diagnosis of skin diseases, for example, melanomas. However, the automated SLS is challenging due to variations in the nature of skin diseases, particularly the ambiguities of boundaries of skin lesion areas (SLAs) and occlusions by hairs. Though many deep learning models, represented by UNet, have been successfully applied to the SLS over the past years, most suffer from inaccurate SLA segmentation with heavy model parameters and slow inferencing speed, hindering their practical applications. To address these challenges, a lightweight SLS network based on structural re‐parameterization and parallel axial shift multilayer perceptron (MLP), called SRP&PASMLP‐Net, is proposed to achieve powerful segmentation performance with fast inference ability. Specifically, a kind of diverse convolution module, the Re‐parameterization Diversity Convolution (RDC) block, is proposed in the shallow stage of the segmentation network to enrich the feature space. The inference is performed by structural re‐parameterization techniques to decouple the structure at the training and inference stages, thus improving the performance of the model without any inference time cost. In addition, a parallel axial shift MLP (PASMLP) module is proposed, which captures local dependencies by axially shifting the channels of the feature map to capture the information flow from different axes and reduce the network parameters and computational complexity effectively. Extensive experimental results show that the proposed SRP&PASMLP‐Net can provide a 4‐fold improvement in the inference speed and a 17‐fold reduction in model parameters compared with the benchmark network, UNet. It outperforms the state‐of‐the‐art methods on two public datasets, that is, ISIC2018 and PH2, for the SLS.