基于视觉语言基础模型的全切片图像皮肤肿瘤零样本分割
Zero-shot segmentation of skin tumors in whole-slide images with vision-language foundation models
AI总结：
本研究提出ZEUS框架，利用视觉语言基础模型实现全切片图像中皮肤肿瘤的零样本分割，减少标注负担并提高分割精度。
摘要：
准确标注皮肤肿瘤活检样本代表了一个重大挑战，因为其形态学变异广泛，组织学模式重叠，且良性与恶性病变之间的细微差别难以区分。视觉语言基础模型（VLMs）在配对的图像-文本语料上预训练，学习将视觉特征与诊断术语联合表示，从而在没有像素级标签的情况下实现组织区域的零样本定位和分类。然而，现有大多数在组织病理学中的VLM应用仍局限于切片级任务或依赖于粗粒度的交互提示，且在处理全像素切片图像（WSIs）时难以生成细粒度的分割。在本工作中，我们介绍了一种针对全切片图像的零样本视觉语言分割流程（ZEUS），这是一个完全自动化的零样本分割框架，利用类特定文本提示集合和冻结的VLM编码器，生成全切片图像中的高分辨率肿瘤掩码。通过将每个WSI分割为重叠的块，提取视觉嵌入，并计算与文本提示的余弦相似度，我们生成最终的分割掩码。我们在两个内部数据集中展示了具有竞争力的性能，即原发梭形细胞肿瘤和皮肤转移，突显了提示设计、领域转移和机构差异在VLMs在组织病理学中的影响。ZEUS显著减少了标注负担，同时为下游诊断工作流程提供了可扩展且可解释的肿瘤界定。
英文摘要：
Accurate annotation of cutaneous neoplasm biopsies represents a major challenge due to their wide morphological variability, overlapping histological patterns, and the subtle distinctions between benign and malignant lesions. Vision-language foundation models (VLMs), pre-trained on paired image-text corpora, learn joint representations that bridge visual features and diagnostic terminology, enabling zero-shot localization and classification of tissue regions without pixel-level labels. However, most existing VLM applications in histopathology remain limited to slide-level tasks or rely on coarse interactive prompts, and they struggle to produce fine-grained segmentations across gigapixel whole-slide images (WSIs). In this work, we introduce a zero-shot visual-language segmentation pipeline for whole-slide images (ZEUS), a fully automated, zero-shot segmentation framework that leverages class-specific textual prompt ensembles and frozen VLM encoders to generate high-resolution tumor masks in WSIs. By partitioning each WSI into overlapping patches, extracting visual embeddings, and computing cosine similarities against text prompts, we generate a final segmentation mask. We demonstrate competitive performance on two in-house datasets, primary spindle cell neoplasms and cutaneous metastases, highlighting the influence of prompt design, domain shifts, and institutional variability in VLMs for histopathology. ZEUS markedly reduces annotation burden while offering scalable, explainable tumor delineation for downstream diagnostic workflows.
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