TFViT: Triplet Focal Vision Transformer Driven by Internet of Medical Things for Leukocyte Classification in Acute Myeloid Leukemia
Xiaojie Xie, Changjiu Liang, Mingjing Wang, Min Dong · IEEE Internet of Things Journal · 2025
Deep learning enables the rapid and accurate identification of cell types in peripheral blood smears of acute myeloid leukemia (AML), greatly facilitating its diagnosis. However, current deep learning-based leukocyte classification models face several limitations. Firstly, traditional leukocyte classification models primarily use convolutional neural networks (CNN) as feature extraction modules. While CNN excel at extracting local features of leukocytes, they are less effective at capturing the overall morphological characteristics of the cells. Additionally, pathological leukocyte samples (e.g., certain subtypes in AML) are often scarce, which introduces class bias and significantly reduces the model’s classification performance on these rare categories. To address these challenges, we propose the Triplet Focal Vision Transformer (TFViT) for leukocyte classification in AML within the context of the Internet of Medical Things (IoMT). The TFViT model leverages Vision Transformers (ViT) and self-attention mechanisms to effectively extract high-quality features from leukocyte images, capturing subtle yet critical morphological differences among various subtypes. Moreover, the integration of Triplet Loss enhances the learning of relative relationships between samples, thereby improving the model’s ability to distinguish between similar categories and fine-grained features. Additionally, by incorporating Focal Loss during training, the model prioritizes minority class samples and reduces the dominance of majority classes in the loss calculation. Furthermore, the relative feature constraints of Triplet Loss do not entirely depend on label distribution, providing an alternative approach to mitigating the impact of class imbalance on classification performance. Experimental results demonstrate that the TFViT model achieves outstanding performance in the leukocyte classification task for AML. Specifically, the Precision, Accuracy, and F1-Score values of the TFViT model are 0.957, 0.962, and 0.957, respectively.