Few Shot Learning for Medical Imaging: A Review of Categorized Images

Deep learning systems have advanced significantly in numerous medical applications, improving various aspects of patient care. However, they still need to work on the issue of dependence on the availability of training data. Few-shot learning (FSL) is a topic of active study that aims to overcome this limitation. FSL techniques require only a few labeled examples for training. FSL-based Medical Imaging (MI) approaches show great potential because many unknown rare diseases have limited annotated imaging data in the real world. In this study, we conducted a systematic review to discover the state of FSL techniques for medical images. We categorized different types of images, such as X-rays, computed tomography (CT), magnetic resonance imaging (MRI), tissues, and other images.