Article: Improving the performance of computer-aided diagnosis systems using semi-supervised learning: a survey and analysis Journal: International Journal of Intelligent Information and Database Systems (IJIIDS) 2020 Vol.13 No.2/3/4 pp.454 - 478 Abstract: The healthcare sector generates important amount of medical data on a daily basis, several machine learning (ML) methods have been developed and studied in order to usefully exploit this substantial sum of information generated colossally, in a wide range practical data mining applications. Yet, an essential key when it comes to developing a competent computer-aided diagnosis (CAD) system is the supervision of data, made by expert annotators; a labelling process considered as a challenging task; as it is both very time consuming and expensive. This survey paper provides the influence of semi-supervised learning framework as it addresses the scarcity of the supervised data for the development of computer-aided diagnosis systems. The methods used and results obtained are discussed and key findings are highlighted. Further, in the light of this review some directions for future research are given; we present a proposed approach using a semi-supervised technique as a core for the learning of a case-based reasoning (CBR) system in CAD context. Inderscience Publishers - linking academia, business and industry through research

Title: Improving the performance of computer-aided diagnosis systems using semi-supervised learning: a survey and analysis

Authors: Asma Chebli; Akila Djebbar; Hayet Farida Djellali Merouani

Addresses: LRI Laboratory, Department of Computer Science, Badji Mokhtar University, BP 12, 23000, Annaba, Algeria ' LRI Laboratory, Department of Computer Science, Badji Mokhtar University, BP 12, 23000, Annaba, Algeria ' LRI Laboratory, Department of Computer Science, Badji Mokhtar University, BP 12, 23000, Annaba, Algeria

Abstract: The healthcare sector generates important amount of medical data on a daily basis, several machine learning (ML) methods have been developed and studied in order to usefully exploit this substantial sum of information generated colossally, in a wide range practical data mining applications. Yet, an essential key when it comes to developing a competent computer-aided diagnosis (CAD) system is the supervision of data, made by expert annotators; a labelling process considered as a challenging task; as it is both very time consuming and expensive. This survey paper provides the influence of semi-supervised learning framework as it addresses the scarcity of the supervised data for the development of computer-aided diagnosis systems. The methods used and results obtained are discussed and key findings are highlighted. Further, in the light of this review some directions for future research are given; we present a proposed approach using a semi-supervised technique as a core for the learning of a case-based reasoning (CBR) system in CAD context.

Keywords: labelled data; unlabelled data; semi-supervised learning; SSL; active learning; sample selection; computer-aided diagnosis; CAD; case-based reasoning; CBR.

DOI: 10.1504/IJIIDS.2020.109466

International Journal of Intelligent Information and Database Systems, 2020 Vol.13 No.2/3/4, pp.454 - 478

Received: 01 Apr 2019
Accepted: 02 Feb 2020
Published online: 09 Sep 2020 *

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Title: Improving the performance of computer-aided diagnosis systems using semi-supervised learning: a survey and analysis

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