ACS-SegNet: An Attention-Based CNN-SegFormer Segmentation Network for Tissue Segmentation in Histopathology

2025/10/23 by Torbati, Nima, Meshcheryakova, Anastasia, Woitek, Ramona +2 · 1 citation
#Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences

paper · doi:10.48550/arxiv.2510.20754

Abstract

Automated histopathological image analysis plays a vital role in computer-aided diagnosis of various diseases. Among developed algorithms, deep learning-based approaches have demonstrated excellent performance in multiple tasks, including semantic tissue segmentation in histological images. In this study, we propose a novel approach based on attention-driven feature fusion of convolutional neural networks (CNNs) and vision transformers (ViTs) within a unified dual-encoder model to improve semantic segmentation performance. Evaluation on two publicly available datasets showed that our model achieved μIoU/μDice scores of 76.79%/86.87% on the GCPS dataset and 64.93%/76.60% on the PUMA dataset, outperforming state-of-the-art and baseline benchmarks. The implementation of our method is publicly available in a GitHub repository: https://github.com/NimaTorbati/ACS-SegNet

Citations

• A Multi-Stage Auto-Context Deep Learning Framework for Tissue and Nuclei Segmentation and Classification in H&E-Stained Histological Images of Advanced Melanoma
• CellViT: Vision Transformers for Precise Cell Segmentation and Classification
• Trans2Unet: Neural fusion for Nuclei Semantic Segmentation
• SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers
• TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation
• CBAM: Convolutional Block Attention Module
• U-Net: Convolutional Networks for Biomedical Image Segmentation

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