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- W4313281286 abstract "Leukemia is a common, multiple and dangerous blood disease, whose early diagnosis and treatment are very important. At present, the diagnosis of leukemia heavily relies on morphological examination of blood cell images by pathologists, which is tedious and time-consuming. Meanwhile, the diagnostic results are highly subjective, which may lead to misdiagnosis and missed diagnosis. To address the gap above, we proposed an improved Vision Transformer model for blood cell recognition. First, a faster R-CNN network was used to locate and extract individual blood cell slices from original images. Then, we split the single-cell image into multiple image patches and put them into the encoder layer for feature extraction. Based on the self-attention mechanism of the Transformer, we proposed a sparse attention module which could focus on the discriminative parts of blood cell images and improve the fine-grained feature representation ability of the model. Finally, a contrastive loss function was adopted to further increase the inter-class difference and intra-class consistency of the extracted features. Experimental results showed that the proposed module outperformed the other approaches and significantly improved the accuracy to 91.96% on the Munich single-cell morphological dataset of leukocytes, which is expected to provide a reference for physicians' clinical diagnosis.白血病是一种常见多发且较为凶险的血液疾病，其早期发现与治疗至关重要。目前白血病类型的诊断主要依靠病理医师对血细胞图像进行形态学检查，该过程枯燥、费时，且诊断结果有较强的主观性，易发生误诊与漏诊。针对上述问题，本文提出了一种基于改进Vision Transformer的血细胞图像识别方法。首先，使用快速区域卷积神经网络从图像中定位并裁剪出单个血细胞图像切片。然后，将单细胞图像划分为多个图像块并输入到编码层中进行特征提取。本文基于Transformer的自注意机制提出了稀疏注意力模块，该模块能够筛选出图像中的辨识性区域，进一步提升模型的细粒度特征表达能力。最后，本文采用对比损失函数，进一步增加分类特征的类内一致性与类间差异性。实验结果表明，本文模型在慕尼黑血细胞形态学数据集上的识别准确率为91.96%，有望为医师临床诊断提供参考依据。." @default.
- W4313281286 created "2023-01-06" @default.
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- W4313281286 date "2022-12-25" @default.
- W4313281286 modified "2023-10-16" @default.
- W4313281286 title "[An improved Vision Transformer model for the recognition of blood cells]." @default.
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- W4313281286 doi "https://doi.org/10.7507/1001-5515.202203008" @default.
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