Augmenting vascular disease diagnosis by vasculature-aware unsupervised learning


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.

A preprint version of the article is available at bioRxiv.

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Fig. 1: The augmentation principle of vascular disease diagnosis.
Fig. 2: VasNet architecture.
Fig. 3: Performance evaluation of VasNet and other existing techniques.
Fig. 4: Augmenting the diagnosis of mouse cerebral vascular disease.
Fig. 5: Augmenting the diagnosis of bleeding from dynamic DSA data.
Fig. 6: Augmenting the diagnosis of bleeding from static DSA data in the human abdomen.
Fig. 7: Discovering a new diagnosis of mouse bowel vascular disease.

Data availability

A pretrained model and all experimental images appearing in the Article and the Supplementary Information are publicly available at Other detailed data that support the findings of this study are available from the corresponding author upon reasonable request.

Code availability

Our PyTorch implementation of VasNet, training data and test samples are available from GitHub ( or


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We thank H. Fan for insightful suggestions regarding the optics experiments, X. Yuan for camera troubleshooting, J. Cai for fruitful discussions about data processing in the bowel vasculature section and H. Zhao for drawing illustrations. The work was supported by the National Natural Science Foundation of China (grants 61722209, 6181001011, 61971255 and 81872368), funds from the Shenzhen Science and Technology Innovation Committee (grants KQJSCX20180327143623167 and JCYJ20180508152130899) and a fund from Shenzhen Development and Reform Commission Subject Construction Project [2017]143.

Author information




L.F., S.M., Q.D. and L.H. conceived this project. S.M., L.F., Y.W., S.J. and M.J. designed the experiments. Y.W. and S.J. performed animal experiments. M.J. innovated and implemented the algorithm. M.J., X.W., Y.W. and J.F. tested the algorithm and performed the data analysis. F.D. contributed the X-ray and DSA data under ethical approval from the PLA General Hospital. Q.D. oversaw the experimental design and progress. S.M., L.F., M.J., Y.W. and J.W. prepared the manuscript. All authors reviewed the paper.

Corresponding authors

Correspondence to Shaohua Ma or Lu Fang or Qionghai Dai.

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The authors declare no competing interests.

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Supplementary information

Supplementary Information

Supplementary Figs. 1–14, Discussion and Table 1.

Reporting Summary

Supplementary Video 1

In vivo fluorescence imaging of cerebral vasculature of a healthy mouse at a 25 Hz frame acquisition rate. As ICG is injected, VasNet recovers the dynamic changes of vascular structure of a healthy cerebrum.

Supplementary Video 2

In vivo fluorescence imaging of cerebral vasculature of a mouse with thrombosis model at a 25 Hz frame acquisition rate. As ICG is injected, VasNet recovers the dynamic changes of vascular structure of a diseased cerebrum.

Supplementary Video 3

Augmenting diagnosis of bleeding from dynamic DSA vascular images acquired from human. VasNet outputs image with vasculature reconstructed. The bleeding features are augmented along with the dynamics of dilation.

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Wang, Y., Ji, M., Jiang, S. et al. Augmenting vascular disease diagnosis by vasculature-aware unsupervised learning. Nat Mach Intell 2, 337–346 (2020).

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