Weakly supervised machine learning

Abstract

Supervised learning aims to build a function or model that seeks as many mappings as possible between the training data and outputs, where each training data will predict as a label to match its corresponding ground-truth value. Although supervised learning has achieved great success in many tasks, sufficient data supervision for labels is not accessible in many domains because accurate data labelling is costly and laborious, particularly in medical image analysis. The cost of the dataset with ground-truth labels is much higher than in other domains. Therefore, it is noteworthy to focus on weakly supervised learning for medical image analysis, as it is more applicable for practical applications. In this review, the authors give an overview of the latest process of weakly supervised learning in medical image analysis, including incomplete, inexact, and inaccurate supervision, and introduce the related works on different applications for medical image analysis. Related concepts are illustrated to help readers get an overview ranging from supervised to unsupervised learning within the scope of machine learning. Furthermore, the challenges and future works of weakly supervised learning in medical image analysis are discussed.