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Yinghuan Shi
Researcher at Nanjing University
Publications - 110
Citations - 3296
Yinghuan Shi is an academic researcher from Nanjing University. The author has contributed to research in topics: Segmentation & Image segmentation. The author has an hindex of 22, co-authored 110 publications receiving 2126 citations. Previous affiliations of Yinghuan Shi include University of Wollongong & University of North Carolina at Chapel Hill.
Papers
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Journal ArticleDOI
Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation, and Diagnosis for COVID-19
Feng Shi,Jun Wang,Jun Shi,Ziyan Wu,Qian Wang,Zhenyu Tang,Kelei He,Yinghuan Shi,Dinggang Shen +8 more
TL;DR: This review paper covers the entire pipeline of medical imaging and analysis techniques involved with COVID-19, including image acquisition, segmentation, diagnosis, and follow-up, and particularly focuses on the integration of AI with X-ray and CT, both of which are widely used in the frontline hospitals.
Journal ArticleDOI
Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation and Diagnosis for COVID-19
Feng Shi,Jun Wang,Jun Shi,Ziyan Wu,Qian Wang,Zhenyu Tang,Kelei He,Yinghuan Shi,Dinggang Shen +8 more
TL;DR: In this article, the authors reviewed the rapid responses in the community of medical imaging (empowered by AI) toward COVID-19, including image acquisition, segmentation, diagnosis, and follow-up.
Journal ArticleDOI
Ea-GANs: Edge-Aware Generative Adversarial Networks for Cross-Modality MR Image Synthesis
TL;DR: The experimental results demonstrate that the proposed edge-aware generative adversarial networks (Ea-GANs) outperform multiple state-of-the-art methods for cross-modality MR image synthesis in both qualitative and quantitative measures.
Proceedings ArticleDOI
A Novel Unsupervised Camera-Aware Domain Adaptation Framework for Person Re-Identification
TL;DR: Zhang et al. as mentioned in this paper proposed an end-to-end deep domain adaptation framework to address the data distribution discrepancy between source and target domains, and the lack of discriminative information in target domain.
Journal ArticleDOI
Pelvic Organ Segmentation Using Distinctive Curve Guided Fully Convolutional Networks
TL;DR: A two-stage deep learning-based method, with a novel distinctive curve-guided fully convolutional network (FCN) designed as a coarse segmentation network to provide region proposals for three pelvic organs to solve the aforementioned challenges.