The wealth of information captured by multiparameter flow cytometry (MFC) can be analyzed by recent methods of computer vision when represented as a single image file. We therefore transformed MFC raw data into a multicolor 2D image by a self‐organizing map and classified this representation using a convolutional neural network. By this means, we built an artificial intelligence that is not only able to distinguish diseased from healthy samples, but it can also differentiate seven subtypes of mature B‐cell neoplasm. We trained our model with 18,274 cases including chronic lymphocytic leukemia and its precursor monoclonal B‐cell lymphocytosis, marginal zone lymphoma, mantle cell lymphoma, prolymphocytic leukemia, follicular lymphoma, hairy cell leukemia, lymphoplasmacytic lymphoma and achieved a weighted F1 score of 0.94 on a separate test set of 2,348 cases. Furthermore, we estimated the trustworthiness of a classification and could classify 70% of all cases with a confidence of 0.95 and higher. Our performance analyses indicate that particularly for rare subtypes further improvement can be expected when even more samples are available for training. © 2020 The Authors. Cytometry Part A published by Wiley Periodicals LLC. on behalf of International Society for Advancement of Cytometry.

中文翻译：

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使用深度学习对多参数流式细胞术数据进行成熟 B 细胞肿瘤的血液学家级别分类。

多参数流式细胞术 (MFC) 捕获的大量信息可以通过最近的计算机视觉方法在表示为单个图像文件时进行分析。因此，我们通过自组织图将 MFC 原始数据转换为多色 2D 图像，并使用卷积神经网络对这种表示进行分类。通过这种方式，我们构建了一个人工智能，它不仅能够区分患病样本和健康样本，还可以区分成熟 B 细胞肿瘤的七种亚型。我们用 18,274 例病例训练了我们的模型，包括慢性淋巴细胞白血病及其前体单克隆 B 细胞淋巴细胞增多症、边缘区淋巴瘤、套细胞淋巴瘤、幼淋巴细胞白血病、滤泡性淋巴瘤、毛细胞白血病、淋巴浆细胞性淋巴瘤，并获得了 0.94 的加权 F1 评分。单独的测试集 2，348例。此外，我们估计了一个分类的可信度，并且可以以 0.95 或更高的置信度对所有案例的 70% 进行分类。我们的性能分析表明，特别是对于稀有亚型，当有更多样本可用于训练时，可以预期进一步改进。© 2020 作者。由 Wiley Periodicals LLC 出版的Cytometry Part A。代表国际细胞计量学促进会。