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.

翻译：深度学习已成为处理许多机器学习任务的重要背景，并在从非结构化数据中提取特征方面展现出突破性提升。尽管这一蓬勃发展的领域在医学图像处理领域持续演进，但问题相关的训练数据稀缺已成为深度学习在医学领域广泛应用面临的重大挑战。为突破有限数据源的限制，研究者开发了一种可通过少量数据解决机器学习问题的模型，称为"小样本学习"。小样本学习算法通过分类与分割方法从少量数据集中提取特征，致力于解决数据限制问题。在医学领域，针对某些机密性疾病常存在可用数据集不足的情况。因此，小样本学习在这一数据稀缺领域备受关注。本章阐述了小样本学习的背景与基本概述，进而描述了小样本学习的分类体系。本文还展示了随时间推移应用于医学图像分析的方法论对比。阐明了当前小样本学习在医学成像领域实施的最新进展，并进一步描述了该领域在医学成像方面的未来发展方向。