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Yi He
Researcher at University of Science and Technology of China
Publications - 5
Citations - 29
Yi He is an academic researcher from University of Science and Technology of China. The author has contributed to research in topics: Deep learning & Domain (software engineering). The author has an hindex of 2, co-authored 5 publications receiving 8 citations.
Papers
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Weakly supervised anomaly segmentation in retinal OCT images using an adversarial learning approach
TL;DR: In this article, a weakly supervised learning network based on CycleGAN was proposed for lesions segmentation in full-width optical coherence tomography (OCT) images, which can accurately detect and segment retinopathy lesions in real-time, without the need for supervised labeling.
Domain adaptation model for retinopathy detection from cross-domain OCT images
TL;DR: A generative adversarial network-based domain adaptation model is proposed to address the cross-domain OCT images classification task, which can extract invariant and discriminative characteristics shared by different domains without incurring additional labeling cost.
Generating Fundus Fluorescence Angiography Images from Structure Fundus Images Using Generative Adversarial Networks
TL;DR: A conditional generative adversarial network (GAN) - based method to directly learn the mapping relationship between structure fundus images and fundus fluorescence angiography images is proposed and local saliency maps, which define each pixel's importance, are used to define a novel saliency loss in the GAN cost function.
Journal ArticleDOI
Unsupervised domain adaptation model for lesion detection in retinal OCT images.
TL;DR: Wang et al. as mentioned in this paper proposed a faster-RCNN based, unsupervised domain adaptation model to address the lesion detection task in cross-device retinal OCT images, which combined a domain classifier with a Wasserstein distance critic to align the shifts at each level.
Posted Content
Generating Fundus Fluorescence Angiography Images from Structure Fundus Images Using Generative Adversarial Networks
TL;DR: Zhang et al. as discussed by the authors proposed a conditional generative adversarial network (GAN) based method to directly learn the mapping relationship between structure fundus images and fundus fluorescence angiography images.