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：深度学习在处理大量机器学习任务中展现出卓越性能，尤其在从非结构化数据中提取特征方面取得了突破性进展。尽管这一蓬勃发展的领域正在推动医学图像处理技术的进步，但依赖特定问题的训练数据匮乏已成为深度学习在医疗领域广泛应用的主要障碍。为解决数据源受限问题，研究者开发了能够利用更少数据解决机器学习问题的模型——"小样本学习"。该算法通过分类与分割方法从少量数据集中提取特征，旨在突破数据局限性。在医疗领域，某些罕见疾病相关的可用数据集往往严重不足。因此，小样本学习在此类数据稀缺场景中备受关注。本章系统介绍了小样本学习的背景与基础概念，继而阐述其分类体系。论文还纵览了不同时期应用于医学图像分析的方法论比较。研究进一步展示了当前小样本学习在医学影像领域的最新进展，并展望了该方向在医疗成像领域的未来发展趋势。