Unsupervised attention-guided domain adaptation model for Acute Lymphocytic Leukemia (ALL) diagnosis.

Acute lymphocytic leukemia (ALL) is a dangerous disease characterized by an increased number of abnormal blood cells in the blood. Its early diagnosis and treatment are crucial, as it can lead to severe consequences if left untreated. Manual examination of blood samples by pathologists and/or hematologists is time-consuming and requires expert skill, so automated and fast solutions need to be developed. However, the marginal data distribution of samples taken from subjects under certain conditions is a major obstacle to building a model that works on datasets obtained under different conditions. Labeling the new dataset also means extra costs. Considering these reasons, this study proposes an attention-enhanced generative adversarial network (GAN) model to move two datasets with different structures into the same feature space. The proposed model is used to transfer the blast and normal cells to the target domain regardless of the background to eliminate the domain difference between the datasets. By learning the complex structure and class features of the cells in an unsupervised manner, the labeling cost is eliminated and it is shown that the trained classifier achieves better results than other domain adaptation methods in the literature. At the end of the study, it was seen that attention mechanisms are highly skilled in extracting the useful parts from the data. In this way, domain mismatch between datasets could be eliminated. • This study aims to address the domain shift problem between two Acute lymphocytic leukemia (ALL) datasets. • An attention mechanism guided domain adaptation method is proposed. • The model performs feature transfer between segmented and unsegmented cells. • The proposed model outperformed the SOTA models. [ABSTRACT FROM AUTHOR]