Transferable automatic hematological cell classification: Overcoming data limitations with self-supervised learning.

Abstract

Background and objective: Classification of peripheral blood and bone marrow cells is critical in the diagnosis and monitoring of hematological disorders. The development of robust and reliable automatic classification systems is hampered by data scarcity and limited model generalizability across laboratories. The present study proposes the integration of self-supervised learning (SSL) into cell classification pipelines to address these challenges.

Methods: The experiments are based on four public hematological single cell image datasets: one bone marrow and three peripheral blood datasets. The cell classification pipeline consists of two parts: (1) SSL-based image feature extraction without the use of image annotations, and (2) a lightweight machine learning classifier applied to the SSL features and trained on only a small number of annotated images.

Results: Direct transfer of SSL models trained on bone marrow data to peripheral blood data resulted in higher balanced classification accuracy than the transfer of supervised deep learning counterparts for all blood datasets. After adaptation of the lightweight machine learning classifier with 50 labeled samples per class of the new dataset, the SSL pipeline surpasses supervised deep learning classification performance for one dataset and classes with rare or atypical cell types and performs similarly on the other datasets.

Conclusions: The results demonstrate that SSL enables (1) extraction of meaningful cell image features without the use of cell class information; (2) efficient transfer of knowledge between bone marrow and peripheral blood cell domains; and (3) efficient model adaptation to new datasets using only a few labeled data samples.