Learn from one data set to classify all - A multi-target domain adaptation approach for white blood cell classification
Küçük Resim Yok
Tarih
2020
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Elsevier Ireland Ltd
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
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. Methods: Only one data set was used as source domain, and an adaptation process was created that made possible the learned knowledge to be used effectively in other domains (multi-target domain adaptation). While constructing the model, we employed data augmentation, data generation and fine-tuning processes, respectively. Results: The proposed model has been able to extract domain-invariant features and achieved high success rates in the tests performed on nine different data sets. Multi-target domain adaptation accuracy was measured as %98.09. Conclusions: At the end of the study, it has been observed that the proposed model ignores the domain differences and it can adapt in a successful way to target domains. In this way, it becomes possible to classify unlabeled samples rapidly by using only a few number of labeled ones. (C) 2020 Elsevier B.V. All rights reserved.
Açıklama
Anahtar Kelimeler
Medical data analysis, White blood cells (WBC), Deep learning, Multi-target domain adaptation, Classification
Kaynak
Computer Methods and Programs in Biomedicine
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
196
