Learn from one data set to classify all - A multi-target domain adaptation approach for white blood cell classification

Baydilli Y. Y., Atila Ü., Elen A.

COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, vol.196, 2020 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 196
  • Publication Date: 2020
  • Doi Number: 10.1016/j.cmpb.2020.105645
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Applied Science & Technology Source, BIOSIS, Biotechnology Research Abstracts, Compendex, Computer & Applied Sciences, EMBASE, INSPEC, MEDLINE
  • Keywords: Medical data analysis, White blood cells (WBC), Deep learning, Multi-target domain adaptation, Classification, SEGMENTATION
  • Gazi University Affiliated: No


Background and objective: Traditional machine learning methods assume that both training and test data come from the same distribution. In this way, it becomes possible to achieve high successes when modelling on the same domain. Unfortunately, in real-world problems, direct transfer between domains is adversely affected due to differences in the data collection process and the internal dynamics of the data. In order to cope with such drawbacks, researchers use a method called "domain adaptation", which enables the successful transfer of information learned in one domain to other domains. In this study, a model that can be used in the classification of white blood cells (WBC) and is not affected by domain differences was proposed.