Weakly supervised machine learning

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