MASG-GAN: A multi-view attention superpixel-guided generative adversarial network for efficient and simultaneous histopathology image segmentation and classification

Abstract

Efficient analysis of Haematoxylin and Eosin stained histopathology images has become a challenge in digital pathology work-flow. We propose a Multi-view Attention Superpixel-guided Generative Adversarial Network (MASG-GAN) to achieve the multi-task learning for nuclei segmentation and benign-malignant tissue classification. Firstly, a novel superpixel segmentation approach driven by Bounded Asymmetric Gaussian Mixture Model (BAGMM) is presented for generating superpixel-prior probability map with high-level semantics. Then, we develop a generator network that integrates student-branch and teacher-branch. Concretely, the teacher-branch takes superpixel-prior probability map as input and guides the student-branch for accurate segmentation and classification. Then in generator, we build a light-weight U-shaped block (LUB) that consists of depthwise separable convolutions with mini encoding-decoding structure to reduce network computational cost. Finally, a Multi-view Attention Module (MVAM) is designed for further enhance the segmentation quality of nuclei with small area and unclear boundary. Extensive experiments on five benchmark datasets demonstrate that our pipeline outperforms some state-of-the-art methods, especially in terms of efficiency.