Abstract Transformer models have demonstrated remarkable success in various fields, but face notable challenges in blood cell classification. These challenges arise primarily from two factors: (1) high computational demands due to the substantial number of parameters, and (2) insufficient labeled data in blood cell datasets, leading to overfitting risks. To address these issues, we introduce BccT, an innovative Transformer-based model specifically designed to optimize the classification of blood cell images. Central to our approach is the Token Fusion module, which intelligently merges similar tokens, thereby enhancing computational efficiency and reducing training overhead without compromising performance. This novel mechanism contrasts with traditional Transformer methods that process each token independently, resulting in significant redundancy. Additionally, we propose the Fixed-Random Classifier to mitigate overfitting risks, particularly in limited-data environments. Unlike conventional classifiers that necessitate constant parameter updates across all layers, our Fixed-Random Classifier comprises only three layers with predominantly static, randomly-initialized parameters, thus reducing dependency on extensive labeled data. Extensive experiments validate the effectiveness of our model. Using ViTbase as the backbone, our model achieves similar accuracy in half the training time compared to the ViTbase model. In experiments on four public blood cell datasets, our model improves accuracy by approximately 4. 5% compared to other state-of-the-art methods.
Ziquan Zhu (Thu,) studied this question.