Research Area: Machine 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.
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Author(s) Name: Yong Wang, Mengqi Ji, Shengwei Jiang, Xukang Wang, Jiamin Wu, Feng Duan, Jingtao Fan, Laiqiang Huang, Shaohua Ma, Lu Fang & Qionghai Dai
Journal name: Nature Machine Intelligence
Conferrence name:
Publisher name: Springer Nature
DOI: 10.1038/s42256-020-0188-z
Volume Information: volume 2, pages 337–346 (2020)
