标题： 基于单元的组织病理学组织分割通过多级特征表示 显示英文标题

标题： Unit-Based Histopathology Tissue Segmentation via Multi-Level Feature Representation

摘要： 我们提出UTS，一种基于单元的组织分割框架，用于组织病理学，它将每个固定大小的32 * 32图块而不是每个像素作为分割单元。 这种方法在不牺牲准确性的情况下减少了注释工作量并提高了计算效率。 为了实现这种方法，我们引入了一种多级视觉变压器（L-ViT），它有助于多级特征表示以捕捉细粒度形态和全局组织上下文。 训练用于将乳腺组织分为三类（浸润性肿瘤、非肿瘤间质和脂肪），UTS支持诸如肿瘤间质定量和手术边缘评估等临床相关任务。 在459个H&E染色区域的386,371个图块上进行评估，其表现优于U-Net变体和基于变压器的基线方法。 代码和数据集将在GitHub上提供。 显示英文摘要

摘要： 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.