Few Shot Learning for Medical Imaging: A Comparative Analysis of Methodologies and Formal Mathematical Framework

TL;DR

This paper reviews few-shot learning techniques in medical imaging, comparing methodologies and providing a formal mathematical framework to address data scarcity issues in medical datasets.

Contribution

It offers a comprehensive overview of few-shot learning methods, their classifications, and a comparison of approaches in medical image analysis, along with future research directions.

Findings

Few-shot learning effectively addresses data scarcity in medical imaging.

Methodological approaches vary over time and application.

Future scope includes improved algorithms and broader applications.

Abstract

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.