A blood cell classification method based on MAE and active learning

• Published in: Biomedical signal processing and control 2024-04, Vol.90, p.105813, Article 105813
• Main Authors: Lu, Qinghang, Wang, Bangyao, He, Quanhui, Zhang, Qingmao, Guo, Liang, Li, Jiaming, Li, Jie, Ma, Qiongxiong
• Format: Article
• Language: English
• Subjects: Active learning; Blood cell classification; Deep learning; Masked autoencoder
• ISSN: 1746-8094; 1746-8108
• DOI: 10.1016/j.bspc.2023.105813

Cell morphology analysis is a crucial diagnostic tool for identifying blood diseases, including acute leukemia. However, the traditional analysis process is time-consuming and requires significant investment in labor and expertise from laboratory doctors. In recent years, deep learning-based automatic blood cell classification techniques have gained popularity. But acquiring image data and annotations in the medical field is often challenging and costly. With the increasing use of deep learning techniques in clinical practice, it has become vital to ensure both accuracy and high-quality annotations. To address these challenges, this paper proposes a blood cell classification method based on Masked Autoencoder (MAE) and active learning (AL), namely MAE4AL. This method utilizes the self-supervised loss of MAE and sample uncertainty to select the most valuable samples for labeling. A comprehensive comparison is conducted between our method and the state-of-the-art blood cell classification technique, which employed ResNeXt. Remarkably, our proposed approach achieves comparable classification performance to ResNeXt when utilizing only 20% of the labeled data. When employing half of the labeled data, our method achieves a classification accuracy of 96.36%, surpassing the ResNeXt model trained with 100% labeled data by 0.79%. •A blood cell classification method is proposed based on MAE and active learning.•Data distribution is extracted more comprehensively by MAE.•High-quality samples are selected through active learning strategy.•Superior and comparable classification performance over state-of-the-art method.