Recent Team Achievement - Graph Attention-Based Material Image Segmentation

That’s a great achievement! Congratulations on the publication of your paper titled “Material Structure Segmentation Method Based on Graph Attention” in the international journal “Materials Today Communications” (IF: 3.662). The School of Computer Engineering and Science at Shanghai University is listed as the first affiliation, with Chen Qiaochuan as the first author, Wei Huishan as the second author, and Associate Professor Han Yuexing as the corresponding author.

With the development and integration of multiple disciplines, the fusion of computer vision and materials science has greatly transformed conventional material research methods. Existing methods can effectively segment images of specific scenes, but there is no universal approach to accurately segment and analyze material images. To address the challenges of complex textures, blurry boundaries, and low contrast in material images, we propose a method that relies on multi-dimensional feature fusion to more effectively train networks with limited and available annotated samples. This architecture consists of an encoder, graph attention module, multi-scale feature fusion module, and decoder. We demonstrate that such a network can be trained end-to-end from images. In electron microscopy images, the segmentation results outperform many previous state-of-the-art methods. With this approach, we can accurately identify multiple structures in material images, providing valuable insights for multi-stage segmentation of material images and exploring new mechanisms for structural transformations in materials science.

Essay: Material structure segmentation method based on graph attention