FAST：面向全切片图像分类的双层少样本学习范式
FAST: A Dual-tier Few-Shot Learning Paradigm for Whole Slide Image Classification
AI总结：
FAST提出双层少样本学习范式，通过极少标注的图像块构建缓存分支和先验分支，充分挖掘WSI信息，以0.22%标注成本接近全监督分类性能。
摘要：
昂贵的细粒度标注和数据稀缺已成为基于深度学习的全切片图像（WSI）分类算法在临床实践中广泛采用的主要障碍。与自然图像中可以利用每张图像标签的少样本学习方法不同，现有的少样本WSI分类方法为了避免昂贵的细粒度标注，仅利用少量细粒度标签或弱监督的切片级标签进行训练。它们缺乏对可用WSI的充分挖掘，严重限制了WSI分类性能。为解决上述问题，我们提出了一种新颖且高效的双层少样本学习范式用于WSI分类，命名为FAST。FAST由一个双层标注策略和一个双分支分类框架组成。首先，为了避免昂贵的细粒度标注，我们在切片级别收集极少量的WSI，并标注极少量的图像块。然后，为了充分挖掘可用的WSI，我们使用所有图像块和可用的图像块标签构建一个缓存分支，该分支利用已标注的图像块来学习未标注图像块的标签，并通过知识检索进行图像块分类。除了缓存分支之外，我们还构建了一个包含可学习提示向量的先验分支，利用视觉-语言模型的文本编码器进行图像块分类。最后，我们整合两个分支的结果以实现WSI分类。在二分类和多分类数据集上的大量实验表明，我们提出的方法显著超越了现有的少样本分类方法，并且仅以0.22%的标注成本接近全监督方法的准确率。所有代码和模型将在https://github.com/fukexue/FAST上公开。
英文摘要：
The expensive fine-grained annotation and data scarcity have become the primary obstacles for the widespread adoption of deep learning-based Whole Slide Images (WSI) classification algorithms in clinical practice. Unlike few-shot learning methods in natural images that can leverage the labels of each image, existing few-shot WSI classification methods only utilize a small number of fine-grained labels or weakly supervised slide labels for training in order to avoid expensive fine-grained annotation. They lack sufficient mining of available WSIs, severely limiting WSI classification performance. To address the above issues, we propose a novel and efficient dual-tier few-shot learning paradigm for WSI classification, named FAST. FAST consists of a dual-level annotation strategy and a dual-branch classification framework. Firstly, to avoid expensive fine-grained annotation, we collect a very small number of WSIs at the slide level, and annotate an extremely small number of patches. Then, to fully mining the available WSIs, we use all the patches and available patch labels to build a cache branch, which utilizes the labeled patches to learn the labels of unlabeled patches and through knowledge retrieval for patch classification. In addition to the cache branch, we also construct a prior branch that includes learnable prompt vectors, using the text encoder of visual-language models for patch classification. Finally, we integrate the results from both branches to achieve WSI classification. Extensive experiments on binary and multi-class datasets demonstrate that our proposed method significantly surpasses existing few-shot classification methods and approaches the accuracy of fully supervised methods with only 0.22$\%$ annotation costs. All codes and models will be publicly available on https://github.com/fukexue/FAST.
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。
