融合CNN与视觉变换器的注意力机制，提升病理图像组织分割精度
ACS-SegNet: An Attention-Based CNN-SegFormer Segmentation Network for Tissue Segmentation in Histopathology
- 设计双编码器结构，通过注意力机制融合CNN与ViT特征
- 在GCPS和PUMA数据集上达到76.79% μIoU和64.93% μIoU
- 适合需要高精度组织分割的病理图像分析研究者使用
自动化组织病理图像分析在多种疾病的计算机辅助诊断中具有关键作用。深度学习方法在语义组织分割等任务中表现优异。本文提出一种基于注意力驱动的卷积神经网络（CNN）与视觉变换器（ViT）特征融合的统一双编码器模型，以提升分割性能。在两个公开数据集上的评估显示，该模型在GCPS数据集上取得76.79%的μIoU和86.87%的μDice，在PUMA数据集上达到64.93%的μIoU和76.60%的μDice，优于当前最优及基线模型。代码已开源：https://github.com/NimaTorbati/ACS-SegNet
原文摘要 · Abstract (English)
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
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