Hairong Zheng

Prof. Dr. Hairong Zheng

Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences1068 Xueyuan Ave, Nanshan, Shenzhen 518055, China

Research Focus & Metrics

Academic Career Length

20Years (2007-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 October 2014 in Progress in Materials Science
Verified
Journal Article
Closed access
Qifa ZhouQifa Zhou
Qifa Zhou
KLKwok Ho Lam
Kwok Ho Lam
HZHairong Zheng
Hairong Zheng
WQWeibao Qiu
Weibao Qiu
KSK. Kirk Shung
K. Kirk Shung

10 July 2018 in Advanced Materials
Verified
Journal Article
Closed access
PDF file
Bing GuoBing Guo
Bing Guo
ZSZonghai Sheng
Zonghai Sheng
DHDehong Hu
Dehong Hu
CLChengbo Liu
Chengbo Liu
HZHairong Zheng
Hairong Zheng
BLBin Liu
Bin Liu

8 October 2021 in Nature Communications
Verified
Journal Article
Open access
PDF file
SWShanshan Wang
Shanshan Wang
CLCheng Li
Cheng Li
RWRongpin Wang
Rongpin Wang
ZLZaiyi Liu
Zaiyi Liu
MWMeiyun Wang
Meiyun Wang
HZHairong Zheng
Hairong Zheng
Automatic medical image segmentation plays a critical role in scientific research and medical care. Existing high-performance deep learning methods typically rely on large training datasets with high-quality manual annotations, which are difficult to obtain in many clinical applications. Here, we introduce Annotation-effIcient Deep lEarning (AIDE), an open-source framework to handle imperfect training datasets. Methodological analyses and empirical evaluations are conducted, and we demonstrate that AIDE surpasses conventional fully-supervised models by presenting better performance on open datasets possessing scarce or noisy annotations. We further test AIDE in a real-life case study for breast tumor segmentation. Three datasets containing 11,852 breast images from three medical centers are employed, and AIDE, utilizing 10% training annotations, consistently produces segmentation maps comparable to those generated by fully-supervised counterparts or provided by independent radiologists. The 10-fold enhanced efficiency in utilizing expert labels has the potential to promote a wide range of biomedical applications.

28 May 2018 in Advanced Materials
Verified
Journal Article
Closed access
PDF file
ZSZonghai Sheng
Zonghai Sheng
Bing GuoBing Guo
Bing Guo
DHDehong Hu
Dehong Hu
SXShidang Xu
Shidang Xu
WWWenbo Wu
Wenbo Wu
BLBin Liu
Bin Liu

1 November 2019 in iScience
Verified
Journal Article
Open access
ZQZhihai Qiu
Zhihai Qiu
JGJinghui Guo
Jinghui Guo
SKShashwati Kala
Shashwati Kala
JZJiejun Zhu
Jiejun Zhu
QXQuanxiang Xian
Quanxiang Xian
LSLei Sun
Lei Sun
Ultrasound brain stimulation is a promising modality for probing brain function and treating brain disease non-invasively and with high spatiotemporal resolution. However, the mechanism underlying its effects remains unclear. Here, we examine the role that the mouse piezo-type mechanosensitive ion channel component 1 (Piezo1) plays in mediating the in vitro effects of ultrasound in mouse primary cortical neurons and a neuronal cell line. We show that ultrasound alone could activate heterologous and endogenous Piezo1, initiating calcium influx and increased nuclear c-Fos expression in primary neurons but not when pre-treated with a Piezo1 inhibitor. We also found that ultrasound significantly increased the expression of the important proteins phospho-CaMKII, phospho-CREB, and c-Fos in a neuronal cell line, but Piezo1 knockdown significantly reduced this effect. Our findings demonstrate that the activity of mechanosensitive ion channels such as Piezo1 stimulated by ultrasound is an important contributor to its ability to stimulate cells in vitro.

1 February 2016 in Journal of Controlled Release
Verified
Journal Article
Closed access
FYFei Yan
Fei Yan
HWHao Wu
Hao Wu
HLHongmei Liu
Hongmei Liu
ZDZhiting Deng
Zhiting Deng
HLHong Liu
Hong Liu
HZHairong Zheng
Hairong Zheng