In machine learning for image-based medical diagnostics, supervised convolutional neural networks are typically trained with large and expertly annotated datasets obtained using high-resolution imaging systems. Moreover, the network’s performance can degrade substantially when applied to a dataset with a different distribution. Here, we show that adversarial learning can be used to develop high-performing networks trained on unannotated medical images of varying image quality. Specifically, we used low-quality images acquired using inexpensive portable optical systems to train networks for the evaluation of human embryos, the quantification of human sperm morphology and the diagnosis of malarial infections in the blood, and show that the networks performed well across different data distributions. We also show that adversarial learning can be used with unlabelled data from unseen domain-shifted datasets to adapt pretrained supervised networks to new distributions, even when data from the original distribution are not available. Adaptive adversarial networks may expand the use of validated neural-network models for the evaluation of data collected from multiple imaging systems of varying quality without compromising the knowledge stored in the network.

在基于图像的医疗诊断的机器学习中，监督卷积神经网络通常使用高分辨率成像系统获得的大型且经过专业注释的数据集进行训练。此外，当应用于具有不同分布的数据集时，网络的性能可能会大幅下降。在这里，我们展示了对抗性学习可用于开发针对不同图像质量的未注释医学图像进行训练的高性能网络。具体来说，我们使用廉价的便携式光学系统获取的低质量图像来训练网络，以评估人类胚胎、量化人类精子形态和诊断血液中的疟疾感染，并表明该网络在不同数据上表现良好分布。我们还表明，即使原始分布中的数据不可用，对抗性学习也可以与来自未见的域转移数据集中的未标记数据一起使用，以使预训练的监督网络适应新的分布。自适应对抗网络可以扩展经过验证的神经网络模型的使用，以评估从不同质量的多个成像系统收集的数据，而不会损害网络中存储的知识。