Semi-Supervised Learning for Medical Application: A Survey

Abstract

Developing a competent and accurate Computer-Aided Diagnosis (CAD) system to assist medical experts in making diagnosis requires a substantial amount of labeled (diagnosed) samples, however collecting labeled data is very costly and challenging when it comes to expert’s annotation. This task is considered as a burden, and is both very time consuming and expensive. The framework of Semi-Supervised Learning (SSL) approach addresses this problem by taking advantage of the abundant amount of accessible unlabeled(undiagnosed) data together with the few limited labeled data in order to train precise classifiers while requiring less human effort and time. This paper reviews different CAD systems using SSL for numerous tasks; the methods used and results obtained are discussed and key findings are highlighted; to conclude with a presented proposed approach for the development of a CAD system; applying Semi-Supervised learning for the classification of cases in order to improve the performance of Case-Based Reasoning(CBR) system.