This chapter delves into the transformative synergy of few-shot learning and healthcare, elucidating its impact on medical procedures. Anchored in machine learning fundamentals, it establishes a core framework through a review of algorithms. Addressing challenges of small healthcare datasets, the chapter highlights the pivotal role of few-shot learning. Innovative methods like multimodal integration and federated learning enhance model robustness, offering insights into complex healthcare scenarios. Formal mathematical explanations categorize few-shot learning challenges, opening avenues for a deeper understanding and implementation in medical imaging.
Top1. Introduction
The medical field has endured a change because of the deployment of machine learning (ML) techniques, which have altered methods for developing new medications, planning treatments, and diagnosing illnesses. A particular area of machine learning known as few-shot learning (FSL) is applied in situations when there is a shortage of data. It seeks to attain acceptable learning performance despite the training set's small amount of supervised information The input samples in this training set have labels for the matching outputs. As a result, several methods, such as transfer learning, neural networks, and meta-learning, have been developed to improve FSL Wang et. al.(2020).
For supervised learning tasks, a variety of data formats can be used with Few-Shot Learning (FSL). Among them are: (i) Classification: This FSL technique, referred to as N-way-K-shot, counts the number of samples (K) as well as the number of classes (N). On the other hand, one-shot learning (OSL) is employed when there is only one example available, and zero-shot learning (ZSL) is utilised when none are available Qiao et. al. (2017); (ii) Regression: Tasks involving FSL can also be utilised involving regression, in which the objective is to estimate a function using a small sample of input-output pairs this instance, the observed values of the dependent and independent variables are represented in the output and recorded in the input, respectively Zell et. al. (2022). Additionally, FSL is applied to half-supervised or unsupervised learning by weight-orienting prior knowledge and predictions between tasks through reinforcement learning Wang & Chen (2020). The idea of an empirical risk minimizer that lacks reliability is central to the taxonomy of financial literacy. Within this paradigm, the empirical risk—which comes from the model's bad decision-making error decomposition—is used to minimise hypothesis h. The empirical risk can approximate the predicted risk when there are enough supervised samples available, which will result in a good performance and precise predictions Lake et. al. (2015). Numerous techniques have been devised to tackle this issue, and they can be divided into various categories based on how they employ past information. According to Wang and coworkers Wang et. al.(2020). The following categories apply to currently available FSL works: viewpoints depending on which element is increased utilising previous knowledge: (i) Data, by utilising related, labelled, and unlabeled data to use historical information to increase and improve the number of samples can be utilised to leverage the limited amount of data available; (ii) model, which is trained on problems akin to the target problem to gain transferable knowledge and enhance its generalisation abilities; the model, which is constrained by prior knowledge to produce a more condensed hypothesis space between expected and empirical risk; and (iii) When using algorithms particularly created for FSL, predictions can be further refined or It is possible to create new algorithms that can generalise predictions with minimal data. The algorithm, where prior knowledge changes the search method to better reflect empirical and projected risk (Fig. 1) Silva-Mendonça et. al. (2023).
Three essential methods to enhance few-shot prediction accuracy are shown in Fig. 1. First, low data can be supplemented with connected, tagged, and unlabeled data. Secondly, the model can generalise a limited set of data by utilizing insights from similar activities. Finally, an algorithm can be changed for instances with little or no data to improve forecast generality.
A brief introduction to few-shot learning (FSL) theory and practice in the healthcare industry is given, along with an analysis of the challenges and opportunities that machine learning (ML) presents for improving and altering healthcare procedures. A thorough summary of the huge influence of machine learning in healthcare is given in this introduction.