For machine learning-based prognosis and diagnosis of rare diseases, such as
pediatric brain tumors, it is necessary to gather medical imaging data from
multiple clinical sites that may use different devices and protocols. Deep
learning-driven harmonization of radiologic images relies on generative
adversarial networks (GANs). However, GANs notoriously generate pseudo
structures that do not exist in the original training data, a phenomenon known
as "hallucination". To prevent hallucination in medical imaging, such as
magnetic resonance images (MRI) of the brain, we propose a one-shot learning
method where we utilize neural style transfer for harmonization. At test time,
the method uses one image from a clinical site to generate an image that
matches the intensity scale of the collaborating sites. Our approach combines
learning a feature extractor, neural style transfer, and adaptive instance
normalization. We further propose a novel strategy to evaluate the
effectiveness of image harmonization approaches with evaluation metrics that
both measure image style harmonization and assess the preservation of
anatomical structures. Experimental results demonstrate the effectiveness of
our method in preserving patient anatomy while adjusting the image intensities
to a new clinical site. Our general harmonization model can be used on unseen
data from new sites, making it a valuable tool for real-world medical
applications and clinical trials.

我们提出了一种基于神经风格迁移的一次学习方法，用于医学影像的谐调化，以防止医学成像中的幻觉，并有效地保护患者的解剖结构。我们的方法可以应用于新的临床场景和临床试验中的未见数据，成为现实世界医学应用的宝贵工具。