Hematologist‐Level Classification of Mature B‐Cell Neoplasm Using Deep Learning on Multiparameter Flow Cytometry Data

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. In a recent issue of Cytometry Part A , Zhao et al. 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, the authors 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. The authors trained their model with 18,274 cases including chronic lymphocytic leukemia and different lymphomas, and achieved a weighted F1 score of 0.94 on a separate test set of 2,348 cases. Furthermore, the authors estimated the trustworthiness of a classification and could classify 70% of all cases with a confidence of 0.95 and higher.

Max Zhao et al.

Cytometry Part A , 2020

DOI: https://doi.org/10.1002/cyto.a.24159

使用多参数流式细胞仪数据的深度学习对成熟B细胞肿瘤进行血液学家级别分类

当表示为单个图像文件时，可以通过计算机视觉的最新方法来分析多参数流式细胞术（MFC）捕获的大量信息。在最近的《细胞计数法》 A部分中，Zhao等。通过自组织映射将MFC原始数据转换为多色2D图像，并使用卷积神经网络对该表示形式进行分类。通过这种方法，作者建立了一种人工智能，该人工智能不仅能够区分健康样本中的疾病，而且还可以区分出成熟B细胞肿瘤的七个亚型。作者对包括慢性淋巴细胞性白血病和各种淋巴瘤在内的18274例患者进行了模型训练，在2348例患者的单独测试集上获得的加权F1评分为0.94。此外，作者估计了分类的可信度，并且可以对所有案例中的70％进行分类，置信度为0.95或更高。

马克斯·赵等。

细胞计量A部分，2020

DOI：https：//doi.org/10.1002/cyto.a.24159