用32×32图像块替代像素分割，提升效率且不降精度。
Unit-Based Histopathology Tissue Segmentation via Multi-Level Feature Representation
- 以固定大小图像块为分割单元，降低标注与计算开销。
- 在45.9万张切片上实现三类乳腺组织精准分割，优于U-Net和主流Transformer模型。
- 适合病理影像量化分析与手术边缘评估等临床任务。
我们提出一种基于单元的组织分割框架UTS，将32×32像素的图像块作为分割单位，而非传统像素级划分。该方法显著减少人工标注量并提升计算效率，同时保持高精度。为此，我们设计了多层级视觉变压器（L-ViT），通过多层次特征表示捕捉细粒度形态与全局组织上下文信息。模型在459个H&E染色区域共386,371张切片上训练，将乳腺组织划分为浸润性肿瘤、非肿瘤间质和脂肪三类，支持肿瘤-间质定量及手术切缘评估等临床应用。实验表明，其性能超越U-Net及其变体和现有Transformer基线。代码与数据集将公开于GitHub。
原文摘要 · Abstract (English)
We propose UTS, a unit-based tissue segmentation framework for histopathology that classifies each fixed-size 32 * 32 tile, rather than each pixel, as the segmentation unit. This approach reduces annotation effort and improves computational efficiency without compromising accuracy. To implement this approach, we introduce a Multi-Level Vision Transformer (L-ViT), which benefits the multi-level feature representation to capture both fine-grained morphology and global tissue context. Trained to segment breast tissue into three categories (infiltrating tumor, non-neoplastic stroma, and fat), UTS supports clinically relevant tasks such as tumor-stroma quantification and surgical margin assessment. Evaluated on 386,371 tiles from 459 H&E-stained regions, it outperforms U-Net variants and transformer-based baselines. Code and Dataset will be available at GitHub.
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