Shifted Window Vision Transformer for Blood Cell Classification
Shifted Window Vision Transformer for Blood Cell Classification
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DOI：
10.3390/electronics12112442
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发表时间：
2023-05-28
期刊：
影响因子：
2.9
通讯作者：
Zhang, Yudong
中科院分区：
文献类型：
--
作者：
Chen, Shuwen;Lu, Siyuan;Zhang, Yudong
血细胞在人体的新陈代谢中起着重要作用，血细胞的状态可以用于临床诊断，如不同血细胞的比例。因此，血细胞分类是一项首要任务，需要大量时间进行人工分析。计算机视觉的最新进展可能有利于将医生从繁琐的任务中解放出来。提出了一种基于移位窗口视觉变换的血细胞自动分类模型(SW-VIT)。SW-VIT体系结构首先在ImageNet数据集上进行预训练，并在血细胞图像上进行微调以进行分类。为了产生更好的分类结果，采用了两种转移策略。一种是微调整个SW-VIT，另一种是仅微调SW-VIT的线性输出层，而所有其他参数都是冻结的。实验中使用了一个名为BCD_DataSet(血细胞计数和检测)的公共数据集。结果表明，SW-VIT在分类准确率方面优于几种最先进的方法。该软件可应用于日常临床诊断。
Blood cells play an important role in the metabolism of the human body, and the status of blood cells can be used for clinical diagnoses, such as the ratio of different blood cells. Therefore, blood cell classification is a primary task, which requires much time for manual analysis. The recent advances in computer vision can be beneficial to free doctors from tedious tasks. In this paper, a novel automated blood cell classification model based on the shifted window vision transformer (SW-ViT) is proposed. The SW-ViT architecture is firstly pre-trained on the ImageNet dataset and fine-tuned on the blood cell images for classification. Two transfer strategies are employed to generate better classification results. One is to fine-tune the entire SW-ViT, and the other is to only fine-tune the linear output layer of the SW-ViT while all the other parameters are frozen. A public dataset named BCCD_Dataset (Blood Cell Count and Detection) is utilized in the experiments. The results show that the SW-ViT outperforms several state-of-the-art methods in terms of classification accuracy. The proposed SW-ViT can be applied in daily clinical diagnosis.