Glo-VLMs：利用视觉语言模型进行细粒度病变肾小球分类
Glo-VLMs: Leveraging Vision-Language Models for Fine-Grained Diseased Glomerulus Classification
AI总结：
针对肾脏病理中细粒度肾小球亚型分类的挑战，提出Glo-VLMs框架，通过结合病理图像与临床文本提示适配预训练VLMs，在少样本场景下实现有效分类，为数据受限的专业临床研究提供新方法。
摘要：
视觉语言模型（VLMs）在数字病理学中展现出相当大的潜力，但在细粒度、疾病特异性分类任务（如区分肾小球亚型）中，其有效性仍受限。这些亚型间的细微形态差异，加上视觉模式与精确临床术语对齐的难度，使肾脏病理学的自动化诊断极具挑战性。本研究探索如何有效适配大型预训练VLMs以执行细粒度肾小球分类，即使在仅少量标记样本可用的场景下。我们提出Glo-VLMs，这是一个系统框架，旨在探索VLMs在数据受限环境下对细粒度肾小球分类的适配。该方法利用精选病理图像与临床文本提示，促进针对细微肾脏病理亚型的联合图像-文本表示学习。通过在少样本学习范式下评估多种VLMs架构和适配策略，我们探索方法选择和标记数据量如何影响临床相关场景下的模型性能。为确保公平比较，我们使用标准化多分类指标评估所有模型，旨在阐明大型预训练模型在专业临床研究应用中的实际需求和潜力。结果显示，微调VLMs在每类仅8个样本的情况下，达到0.7416的准确率、0.9045的宏AUC和0.5277的F1分数，表明即使在监督高度有限的情况下，基础模型也能有效适配细粒度医学图像分类。
英文摘要：
Vision-language models (VLMs) have shown considerable potential in digital pathology, yet their effectiveness remains limited for fine-grained, disease-specific classification tasks such as distinguishing between glomerular subtypes. The subtle morphological variations among these subtypes, combined with the difficulty of aligning visual patterns with precise clinical terminology, make automated diagnosis in renal pathology particularly challenging. In this work, we explore how large pretrained VLMs can be effectively adapted to perform fine-grained glomerular classification, even in scenarios where only a small number of labeled examples are available. In this work, we introduce Glo-VLMs, a systematic framework designed to explore the adaptation of VLMs to fine-grained glomerular classification in data-constrained settings. Our approach leverages curated pathology images alongside clinical text prompts to facilitate joint image-text representation learning for nuanced renal pathology subtypes. By assessing various VLMs architectures and adaptation strategies under a few-shot learning paradigm, we explore how both the choice of method and the amount of labeled data impact model performance in clinically relevant scenarios. To ensure a fair comparison, we evaluate all models using standardized multi-class metrics, aiming to clarify the practical requirements and potential of large pretrained models for specialized clinical research applications. As a result, fine-tuning the VLMs achieved 0.7416 accuracy, 0.9045 macro-AUC, and 0.5277 F1-score with only 8 shots per class, demonstrating that even with highly limited supervision, foundation models can be effectively adapted for fine-grained medical image classification.
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