Few Shot Learning for Medical Imaging

While deep learning systems have provided breakthroughs in several tasks in the medical domain, they are still limited by the problem of dependency on the availability of training data. To counter this limitation, there is active research ongoing in few shot learning. Few shot learning algorithms aim to overcome the data dependency by exploiting the information available from a very small amount of data. In medical imaging, due to the rare occurrence of some diseases, there is often a limitation on the available data, as a result, to which the success of few shot learning algorithms can prove to be a significant advancement. In this chapter, the background and working of few shot learning algorithms are explained. The problem statement for few shot classification and segmentation is described. There is then a detailed study of the problems faced in medical imaging related to the availability of limited data. After establishing context, the recent advances in the application of few shot learning to medical imaging tasks such as classification and segmentation are explored. The results of these applications are examined with a discussion on its future scope.