Hong Cheng Data-verified
Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.
Researcher
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Biography and Research Information
OverviewAI-generated summary
Hong Cheng's research interests encompass machine learning applications in diverse fields, including computer vision, materials science, and healthcare. In computer vision, Cheng has investigated mutual graph learning and uncertainty-guided transformer reasoning for camouflaged object detection. Their work in materials science involves using machine learning for phase prediction in high entropy alloys. Cheng has also explored millimeter wave path loss modeling for 5G communications using deep learning techniques. Additionally, their research extends to health sciences, with studies on the effects of exoskeleton-assisted walking in individuals with spinal cord injury, in vitro fluidic systems for applying shear stress on endothelial cells, and the development of nomogram models to predict postpartum stress urinary incontinence.
Metrics
- h-index: 38
- Publications: 384
- Citations: 5,917
Selected Publications
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Skin Lesion Segmentation Using Unet With A Topology Term in Loss Function (2025)
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Multi-Class Label Detection and Bounding Box Regression Using Transformer with a Customized Loss Function (2024)
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Object Localization Using Vision Transformer with a Loss Function Based on IOU and Mean Squared Error (2023)
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A Topological Data Analysis-Based Approach to Object Localization: A Comparison with ViT and Yolov7 (2023)
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