Circulating tumour cells (CTCs) found in the blood of cancer patients are a promising biomarker in precision medicine. However, their use is currently hindered by their low frequency, tedious manual scoring and extensive cell heterogeneities. Those challenges limit the effectiveness of classical machine-learning methods for automated CTC analysis. Here, we combine autoencoding convolutional neural networks with advanced visualization techniques. This provides a very informative view on the data that opens the way for new biomedical research questions. We unravel hidden information in the raw image data of fluorescent images of blood samples enriched for CTCs. Our network classifies fluorescent images of single cells in five different classes with an accuracy, sensitivity and specificity of over 96%, and the obtained CTC counts predict the overall survival of cancer patients as well as state-of-the-art manual counts. Moreover, our network excelled in identifying different important subclasses of objects. Deep learning was faster and superior to classical image analysis approaches and enabled the identification of new biological phenomena.

在癌症患者血液中发现的循环肿瘤细胞（CTC）是精密医学中有希望的生物标志物。但是，它们的使用目前因其低频，繁琐的手动评分和广泛的细胞异质性而受到阻碍。这些挑战限制了经典的机器学习方法用于自动CTC分析的有效性。在这里，我们将自动编码卷积神经网络与高级可视化技术结合在一起。这提供了非常有用的数据视图，为新的生物医学研究问题开辟了道路。我们在富集四氯化碳的血样荧光图像的原始图像数据中揭示隐藏信息。我们的网络将单个细胞的荧光图像分为五类，其准确性，敏感性和特异性均超过96％，并且获得的CTC计数可以预测癌症患者的总体生存率以及最新的人工计数。此外，我们的网络擅长识别对象的不同重要子类。深度学习更快且优于经典图像分析方法，并能够识别新的生物现象。