Leveraging commonality across multiple tissue slices for enhanced whole slide image classification using graph convolutional networks.

Journal: BMC medical imaging
Published Date: Jul 1, 2025

Abstract

BACKGROUND: Accurate classification of histopathological whole slide images (WSIs) is essential for cancer diagnosis and treatment planning. Conventional WSI creation involves slicing a biopsy tissue into multiple slices, placing them on a single glass slide, and digitally scanning them. While deep learning approaches have shown promise in WSI analysis, they mostly overlook potential common patterns across different slices of the original tissue.

Authors

  • Sakonporn Noree
    Graduate School of Data Science, Department of Industrial and System Engineering, Korea Advanced Institute of Science and Technology, Deajeon, South Korea.
  • Willmer Rafell Quiñones Robles
    Graduate School of Data Science, Department of Industrial & Systems Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea.
  • Young Sin Ko
    Pathology Center, Seegene Medical Foundation, Seoul, Republic of Korea.
  • Mun Yong Yi
    Graduate School of Data Science, Department of Industrial & Systems Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea.

Keywords

No keywords available for this article.

External Resources

Popular Topics
Recent Journals