USMLP: U-shaped Sparse-MLP network for mass segmentation in mammograms

Abstract

Convolutional neural networks (CNN) are widely used in computer-aided diagnosis (CAD). However, there are several limitations of this structure for mass segmentation in mammograms, which may lead to incorrect segmentation results. Recently, multi-layer perceptron (MLP) based methods introduce a new way to solve computer vision (CV) problems. In this paper we propose U-shaped Sparse-MLP (USMLP), which is a MLP-based segmentation model with U-shaped architecture. Our proposed method consists of CNN layers and sparse MLP (sMLP) blocks. Detailed experiments on two public datasets show that our method achieves state-of-the-art performance while remaining efficiency, compared with other benchmarks. We hope the investigation of new network architecture can be beneficial to mass segmentation tasks as well as CAD systems.