Vascular disease is one of the leading causes of death and threatens human health worldwide. Imaging examination of vascular pathology with reduced invasiveness is challenging due to the intrinsic vasculature complexity and non-uniform scattering from bio-tissues. Here, we report VasNet, a vasculature-aware unsupervised learning algorithm that augments pathovascular recognition from small sets of unlabelled fluorescence and digital subtraction angiography images. VasNet adopts a multi-scale fusion strategy with a domain adversarial neural network loss function that induces biased pattern reconstruction by strengthening features relevant to the retinal vasculature reference while weakening irrelevant features. VasNet delivers the outputs ‘Structure + X’ (where X refers to multi-dimensional features such as blood flows, the distinguishment of blood dilation and its suspicious counterparts, and the dependence of new pattern emergence on disease progression). Therefore, explainable imaging output from VasNet and other algorithm extensions holds the promise to augment medical diagnosis, as it improves performance while reducing the cost of human expertise, equipment and time consumption.

血管疾病是导致死亡的主要原因之一，并且威胁着全世界的人类健康。由于内在的脉管系统的复杂性和生物组织的不均匀散射，以减少的浸润性对血管病理学进行影像学检查具有挑战性。在这里，我们报告VasNet，这是一种可感知脉管系统的无监督学习算法，可从少量未标记的荧光和数字减影血管造影图像中增强对血管的识别。VasNet采用具有域对抗神经网络损失功能的多尺度融合策略，该功能通过增强与视网膜脉管系统参考相关的功能，同时削弱无关的功能，从而诱导偏向模式的重建。VasNet会提供输出“结构+ X”（其中X表示多维特征，例如血流，血液扩张及其可疑对应物的区别，以及新模式的出现对疾病进展的依赖性）。因此，来自VasNet和其他算法扩展的可解释的成像输出有望增强医疗诊断，因为它可以提高性能，同时减少人工成本，设备和时间消耗。