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

Autor:	Hayet Farida Merouani, Asma Chebli, Akila Djebbar
Rok vydání:	2020
Předmět:	Exploit Computer science business.industry Active learning (machine learning) Process (engineering) CAD Context (language use) Semi-supervised learning Machine learning computer.software_genre Key (cryptography) Case-based reasoning Artificial intelligence business computer Information Systems
Zdroj:	International Journal of Intelligent Information and Database Systems. 13:454
ISSN:	1751-5866 1751-5858
DOI:	10.1504/ijiids.2020.10031616
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::273cb1bedffdf05849c596c8968e565c https://doi.org/10.1504/ijiids.2020.10031616 Zobrazit plný text záznamu