提升组织病理学分类的视觉-语言模型：一次性预测全部
Boosting Vision-Language Models for Histopathology Classification: Predict all at once
AI总结：
本文提出一种基于直推式学习的组织病理学分类方法，利用文本预测与图像块亲和关系，在不增加标签的情况下提升多种视觉-语言模型的零样本分类准确率，并实现高效处理。
摘要：
面向组织病理学的视觉-语言模型（VLMs）的发展已展现出有前景的新用途和零样本性能。然而，当前将大切片分解为较小图像块的方法仅专注于归纳式分类，即对每个图像块的预测独立于目标测试数据中的其他图像块。我们通过引入一种直推式方法来扩展这些大模型的能力。通过利用基于文本的预测和图像块之间的亲和关系，我们的方法在不使用任何额外标签的情况下，发挥了这些新型VLMs强大的零样本能力。我们的实验覆盖四个组织病理学数据集和五种不同的VLMs。仅在嵌入空间中运行（即黑盒设置），我们的方法效率极高，仅需几秒钟即可处理 $10^5$ 个图像块，并且相比归纳式零样本分类显示出显著的准确率提升。代码可在 https://github.com/FereshteShakeri/Histo-TransCLIP 获取。
英文摘要：
The development of vision-language models (VLMs) for histo-pathology has shown promising new usages and zero-shot performances. However, current approaches, which decompose large slides into smaller patches, focus solely on inductive classification, i.e., prediction for each patch is made independently of the other patches in the target test data. We extend the capability of these large models by introducing a transductive approach. By using text-based predictions and affinity relationships among patches, our approach leverages the strong zero-shot capabilities of these new VLMs without any additional labels. Our experiments cover four histopathology datasets and five different VLMs. Operating solely in the embedding space (i.e., in a black-box setting), our approach is highly efficient, processing $10^5$ patches in just a few seconds, and shows significant accuracy improvements over inductive zero-shot classification. Code available at https://github.com/FereshteShakeri/Histo-TransCLIP.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
