Zhenhong Du

ORCID: 0000-0001-9670-7403
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About
Contact & Profiles
Research Areas
  • Spatial and Panel Data Analysis
  • Cell Image Analysis Techniques
  • Advanced Fluorescence Microscopy Techniques
  • Regional Economic and Spatial Analysis
  • Cancer-related molecular mechanisms research
  • Urban Transport and Accessibility
  • RNA Research and Splicing
  • Optical Coherence Tomography Applications
  • Circular RNAs in diseases
  • Digital Holography and Microscopy
  • Autoimmune Bullous Skin Diseases
  • Dermatological and Skeletal Disorders
  • Land Use and Ecosystem Services
  • Skin and Cellular Biology Research

Zhejiang University
2022-2024

Southwest Medical University
2022

First Affiliated Hospital Zhejiang University
2022

Affiliated Hospital of North Sichuan Medical College
2018

High-throughput deep tissue imaging and chemical clearing protocols have brought out great promotion in biological research. However, due to uneven transparency introduced by anisotropy imperfectly cleared tissues, fluorescence based on direct still encounters challenges, such as image blurring, low contrast, artifacts so on. Here we reported a three-dimensional virtual optical method unsupervised cycle-consistent generative adversarial network, termed 3D-VoCycleGAN, digitally improve...

10.3389/fphy.2022.965095 article EN cc-by Frontiers in Physics 2022-07-19

Abstract. Spatiotemporal regression is a crucial method in geography for discerning spatiotemporal nonstationarity geographical relationships and has found widespread application across diverse research domains. This study implements two innovative intelligent models, i.e., Geographically Neural Network Weighted Regression (GNNWR) Temporally (GTNNWR), which use neural networks to estimate nonstationarity. Due the higher accuracy generalization ability, these models have been widely used...

10.5194/gmd-17-8455-2024 article EN cc-by Geoscientific model development 2024-11-28

Conventional histopathological examinations are time-consuming and labor-intensive, insufficient to depict 3D pathological features intuitively. Here we report an ultrafast histological imaging scheme based on optimized selective plane illumination microscopy (mSPIM), a minutes-time scale clearing method (FOCM), deep learning-based image enhancement algorithm (SRACNet) realize preparation of clinical tissues. Our enables 1-minute fast (up 900 mm2/min) 200 µm-thick mouse kidney slices at...

10.1364/ol.463705 article EN Optics Letters 2022-08-19
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