The classification of six types of white blood cells (WBCs) is considered essential for leukemia diagnosis, while the classification is labor-intensive and strict with the clinical experience. To relieve the complicated process with an efficient and automatic method, we propose the A ttention-aware R esidual Network based M anifold L earning model (ARML) to classify WBCs. The proposed ARML model leverages the adaptive attention-aware residual learning to exploit the category-relevant image-level features and strengthen the first-order feature representation ability. To learn more discriminatory information than the first-order ones, the second-order features are characterized. Afterwards, ARML encodes both the first- and second-order features with Gaussian embedding into the Riemannian manifold to learn the underlying non-linear structure of the features for classification. ARML can be trained in an end-to-end fashion, and the learnable parameters are iteratively optimized. 10800 WBCs images (1800 images for each type) is collected, 9000 images and five-fold cross-validation are used for training and validation of the model, while additional 1800 images for testing. The results show that ARML achieving average classification accuracy of 0.953 outperforms other state-of-the-art methods with fewer trainable parameters. In the ablation study, ARML achieves improved accuracy against its three variants: without manifold learning (AR), without attention-aware learning (RML), and AR without attention-aware learning. The t-SNE results illustrate that ARML has learned more distinguishable features than the comparison methods, which benefits the WBCs classification. ARML provides a clinically feasible WBCs classification solution for leukemia diagnose with an efficient manner.

中文翻译：

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基于注意力感知残差网络的白细胞分类流形学习

六种类型的白细胞（WBC）的分类被认为是白血病诊断必不可少的，而分类是劳动密集型的，并且对临床经验很严格。为了用一种高效、自动化的方法减轻复杂的过程，我们提出了一种 注意注意 电阻 基于剩余网络 米 折合的 升 收入模型 (ARML) 对 WBC 进行分类。提出的ARML模型利用自适应注意力感知残差学习来利用与类别相关的图像级特征并增强一阶特征表示能力。为了学习比一阶特征更多的判别信息，对二阶特征进行了表征。然后，ARML 将一阶和二阶特征编码为高斯嵌入到黎曼流形中，以学习用于分类的特征的潜在非线性结构。ARML 可以以端到端的方式进行训练，并且可学习的参数被迭代优化。收集了 10800 张 WBCs 图像（每种类型 1800 张图像），9000 张图像和五重交叉验证用于模型的训练和验证，另外 1800 张图像用于测试。结果表明，ARML 实现了 0.953 的平均分类准确度，优于其他具有较少可训练参数的最先进方法。在消融研究中，ARML 针对其三种变体实现了更高的准确性：没有流形学习 (AR)、没有注意力感知学习 (RML) 和没有注意力感知学习的 AR。t-SNE 结果表明，ARML 比比较方法学到了更多可区分的特征，这有利于 WBC 分类。ARML 以有效的方式为白血病诊断提供了临床可行的 WBC 分类解决方案。没有流形学习 (AR)、没有注意力感知学习 (RML) 和没有注意力感知学习的 AR。t-SNE 结果表明，ARML 比比较方法学到了更多可区分的特征，这有利于 WBC 分类。ARML 以有效的方式为白血病诊断提供了临床可行的 WBC 分类解决方案。没有流形学习 (AR)、没有注意力感知学习 (RML) 和没有注意力感知学习的 AR。t-SNE 结果表明，ARML 比比较方法学到了更多可区分的特征，这有利于 WBC 分类。ARML 以有效的方式为白血病诊断提供了临床可行的 WBC 分类解决方案。