Deep learning becomes an elevated context regarding disposing of many machine learning tasks and has shown a breakthrough upliftment to extract features from unstructured data. Though this flourishing context is developing in the medical image processing sector, scarcity of problem-dependent training data has become a larger issue in the way of easy application of deep learning in the medical sector. To unravel the confined data source, researchers have developed a model that can solve machine learning problems with fewer data called ``Few shot learning". Few hot learning algorithms determine to solve the data limitation problems by extracting the characteristics from a small dataset through classification and segmentation methods. In the medical sector, there is frequently a shortage of available datasets in respect of some confidential diseases. Therefore, Few shot learning gets the limelight in this data scarcity sector. In this chapter, the background and basic overview of a few shots of learning is represented. Henceforth, the classification of few-shot learning is described also. Even the paper shows a comparison of methodological approaches that are applied in medical image analysis over time. The current advancement in the implementation of few-shot learning concerning medical imaging is illustrated. The future scope of this domain in the medical imaging sector is further described.

深度学习在医学图像处理领域的应用受到数据不足的限制，因此研究者们开发出了一种称为“Few-shot learning”的模型，通过分类和分割方法来从小规模数据集中提取特征以解决数据不足的问题。本文综述了Few-shot learning的背景和基本概述，介绍了医学图像分析中的方法学策略比较，描述了Few-shot learning在医学图像处理应用的最新进展，并展望了其未来的应用前景。

医学影像的少样本学习：方法学和数学框架的比较分析