MFTrans: A Multi-Resolution Fusion Transformer for Robust Tumor Segmentation in Whole Slide Images

Sungkyu Yang, Woohyun Park, Kwangil Yim, Mansu Kim

Abstract: Accurate tumor segmentation in whole slide image (WSI) is essential for histopathological diagnosis and research, but the traditional manual analysis is labor-intensive and prone to variability. Furthermore, many artificial models focus on specific magnification images, limiting the detailed information available for segmentation. To address these challenges, we propose MFTrans, a novel multi-resolution fusion transformer with a CNN-based architecture designed for efficient tumor segmentation in WSI. Inspired by the diagnostic procedures of expert pathologists, MFTrans integrates both high- and low-magnification images, capturing detailed local features and broader contextual relationships through a dual-branch architecture. The model employs a global token transformer and cross-attention mechanism to fuse hierarchical features from dual branches to improve segmentation performance. We evaluate MFTrans on three real-world WSI datasets: Camelyon16, PAIP2019, and Catholic Uijeongbu St. Mary's hospital dataset, demonstrating its superior segmentation performance over state-of-the-art methods in balanced and imbalanced setups. These results highlight MFTrans's effectiveness in medical image analysis and its generalizability across different datasets, making it a robust tool for automated cancer diagnostics. Our code is available at https://github.com/aimed-gist/MFTrans.

External IDs:dblp:conf/wacv/YangPYK25