Biomedical applications of deep learning algorithms rely on large expert annotated data sets. The classification of bone marrow (BM) cell cytomorphology, an important cornerstone of hematological diagnosis, is still done manually thousands of times every day because of a lack of data sets and trained models. We applied convolutional neural networks (CNNs) to a large data set of 171 374 microscopic cytological images taken from BM smears from 945 patients diagnosed with a variety of hematological diseases. The data set is the largest expert-annotated pool of BM cytology images available in the literature. It allows us to train high-quality classifiers of leukocyte cytomorphology that identify a wide range of diagnostically relevant cell species with high precision and recall. Our CNNs outcompete previous feature-based approaches and provide a proof-of-concept for the classification problem of single BM cells. This study is a step toward automated evaluation of BM cell morphology using state-of-the-art image-classification algorithms. The underlying data set represents an educational resource, as well as a reference for future artificial intelligence-based approaches to BM cytomorphology.

中文翻译：

---

在大型图像数据集上使用深度神经网络高度准确地区分骨髓细胞形态。

深度学习算法的生物医学应用依赖于大型专家注释数据集。骨髓 (BM) 细胞形态学分类是血液学诊断的重要基石，但由于缺乏数据集和训练有素的模型，每天仍需手动完成数千次。我们将卷积神经网络 (CNN) 应用于从 945 名被诊断患有各种血液病的患者的 BM 涂片中获取的 171 374 张显微细胞学图像的大型数据集。该数据集是文献中可用的最大的专家注释 BM 细胞学图像池。它使我们能够训练高质量的白细胞细胞形态学分类器，以高精度和召回率识别广泛的诊断相关细胞种类。我们的 CNN 胜过之前基于特征的方法，并为单个 BM 细胞的分类问题提供了概念验证。这项研究是使用最先进的图像分类算法自动评估 BM 细胞形态的一步。基础数据集代表了一种教育资源，以及未来基于人工智能的 BM 细胞形态学方法的参考。