Hai Lin

Hai Lin

Professfor (State Key Lab of CAD&CG, Zhejiang University)

Biography

Hai Lin is a professor in State Key Lab of CAD&CG, Zhejiang University. He got M.Eng and B.Eng from Xidian University in 1990 and 1987 respectively. He joined in State Key Lab. of CAD&CG in 1990. After he obtained a Ph. D in computer science from Zhejiang University, he had been a Research Fellow in Medical Visualization at De Montfort University, UK from 2000 to 2003. He was a visiting professor of the Department of Computing and Information Systems, University of Bedfordshire, UK. His research interests include computer graphics, scientific visualization, volume rendering, virtual reality and graphical electromagnetic computing.

Announcements

  • PhD and master applicants interested in visualization and graphical electromagnetic computing? Please send me email along with your resume.
  • Undergrads looking for projects? Please send me email along with your resume.
Education

  • Scientific Visualization, 2022

    Zhejiang University

Experience

 
 
 
 
 
Zhejiang University
Professor
Zhejiang University
Jul 1990 – Present China
Interests include computer graphics, scientific visualization, volume rendering, virtual reality and graphical electromagnetic computing.
 
 
 
 
 
De Montfort University
Research Fellow
De Montfort University
Sep 2000 – Jul 2003 UK
 
 
 
 
 
Xidian University
M.Eng and B.Eng
Xidian University
Sep 1983 – Jul 1990 China

Research/Projects

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All Visualization Electromagnetic Computing Medical Imaging
Deep learning for accurate diagnosis of liver tumor based on magnetic resonance imaging and clinical data
Background Early-stage diagnosis and treatment can improve survival rates of liver cancer patients. Dynamic contrast-enhanced MRI provides the most comprehensive information for differential diagnosis of liver tumors. However, MRI diagnosis is affected by subjective experience, so deep learning may supply a new diagnostic strategy.
Deep learning for accurate diagnosis of liver tumor based on magnetic resonance imaging and clinical data
ALA-Net: Adaptive Lesion-Aware Attention Network for 3D Colorectal Tumor Segmentation
Accurate and reliable segmentation of colorectal tumors and surrounding colorectal tissues on 3D magnetic resonance images has critical importance in preoperative prediction, staging, and radiotherapy. Previous works simply combine multilevel features without aggregating representative semantic information and without compensating for the loss of spatial information caused by down-sampling.
ALA-Net: Adaptive Lesion-Aware Attention Network for 3D Colorectal Tumor Segmentation
LRVRG: a local region-based variational region growing algorithm for fast mandible segmentation from CBCT images
This paper proposes a local region-based variational region growing algorithm, which integrates local region and shape prior to segment the mandible accurately. Firstly, we select initial seeds in the CBCT image and then calculate candidate point sets and the local region energy function of each point.
LRVRG: a local region-based variational region growing algorithm for fast mandible segmentation from CBCT images
DeepNFT: Towards Precise Neurofibrillary Tangle Detection via Improving Multi-scale Feature Fusion and Adversary
Detecting neurofibrillary tangles is an important procedure in the assessment of the intensity and distribution pattern of hippocampal tau pathology, which are the principal clinical phenotypes associated with Alzheimer’s disease. Existing deep learning based detectors still face a critical obstacle: the difficulty in detecting extremely small objects in high resolution images.
DeepNFT: Towards Precise Neurofibrillary Tangle Detection via Improving Multi-scale Feature Fusion and Adversary
A GPU-based Radio Wave Propagation Prediction with Progressive Processing on Point Cloud
Point cloud data (PCD) can record a high-resolution model of the environment much more efficiently than the conventional geometrical mesh. This feature makes PCD suitable for the radio channel prediction of a new environment, especially for the millimeter-wave (mmWave).
A GPU-based Radio Wave Propagation Prediction with Progressive Processing on Point Cloud
Visual exploration of software evolution via topic modeling
For various reasons, such as new requirements, architecture refactoring, and bug fixing, software projects often evolve to yield better quality and performance. All changes produced during the development process are reflected in the source code, which provides an opportunity to explore software evolution.
Visual exploration of software evolution via topic modeling
Graph convolutional network-based semi-supervisedfeature classification of volumes
Feature classification has always been one of the research hotspots in scientific visualization.However, conventional interactive feature classification methods rely on prior knowledge and typicallyrequire trial and error, whereas feature classification based on data mining is generally based on localfeatures; therefore, obtaining good results with traditional methods is difficult.
Graph convolutional network-based semi-supervisedfeature classification of volumes
Exploring Evolution of Dynamic Networks via Diachronic Node Embeddings
Dynamic networks evolve with their structures changing over time. It is still a challenging problem to efficiently explore the evolution of dynamic networks in terms of both their structural and temporal properties.
Exploring Evolution of Dynamic Networks via Diachronic Node Embeddings
Solving the Scattering of Combined Conductor-Dielectric Bodies Based on EPA
This work introduces a new method for analyzing the scattering from combined conductor-dielectric bodies based on equivalent principle algorithm (EPA). The object is decomposed into several different parts. In each part, it can be an inhomogeneous dielectric or a perfect electric conductor (PEC) of arbitrary shape.
Solving the Scattering of Combined Conductor-Dielectric Bodies Based on EPA
Electromagnetic Characteristics Optimization Based on Shape Deformation
A feasible approach to optimize the characteristics of electromagnetic (EM) objects through shape deformation is proposed. We provide a complete and detailed procedure of the optimization and investigate the shape deformation formulations for some typical EM objects.
Electromagnetic Characteristics Optimization Based on Shape Deformation
Deep Learning with Attention Mechanism for Electromagnetic Inverse Scattering
The inverse scattering problems (ISPs) aim for reconstructing the unknown targets from the measured scattered fields. Due to its highly nonlinear and ill-posed properties, solving ISPs is a challenging task. In this paper, a new deep learning technique is investigated to improve the solution of electromagnetic inverse scattering problems (ISPs).
Deep Learning with Attention Mechanism for Electromagnetic Inverse Scattering
Voxel2vec
A Natural Language Processing Approcach to Learn Distributed Representations for Scientific Data
PDF
Voxel2vec

Recent Publications

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Han Wang, Mingjie Pang, Hai Lin (2022). Enhanced solution to the surface-volume-surface EFIE for arbitrary metal-dielectric composite objects. Frontiers of Information Technology & Electronic Engineering.

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Xiangyang He, Shuoliu Yang,  Yubo Tao, Haoran Dai, Hai Lin (2022). Graph convolutional network-based semi-supervised feature classification of volumes. Journal of Visualization.

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YanKai Jiang, Ke Xu, Xinyue Wang, Yuan Li, Hongguang Cui,  Yubo Tao, Hai Lin (2022). SatFormer: Saliency-Guided Abnormality-Aware Transformer for Retinal Disease Classification in Fundus Image. Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI 2022, Vienna, Austria, 23-29 July 2022.

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Xiangyang He,  Yubo Tao, Shuoliu Yang, Chuanchang Chen, Hai Lin (2022). ScalarGCN: scalar-value association analysis of volumes based on graph convolutional network. Journal of Visualization.

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Xiangyang He,  Yubo Tao, Shuoliu Yang, Haoran Dai, Hai Lin (2022). voxel2vec: A Natural Language Processing Approach to Learning Distributed Representations for Scientific Data. IEEE Transcation of Visualization and Computer Graphics.

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Mingjie Pang, Han Wang, Kaiwei Lin, Hai Lin (2021). A GPU-based radio wave propagation prediction with progressive processing on point cloud. IEEE Antennas and Wireless Propagation Letters.

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Han Wang, Mingjie Pang, Hai Lin (2021). Accuracy improvement of the algebraic fast methods for the volume-surface integral equation. 2021 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (APS/URSI).

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YanKai Jiang, Shufeng Xu, Hongjie Fan, Jiahong Qian, Weizhi Luo, Shihui Zhen,  Yubo Tao, Jihong Sun, Hai Lin (2021). ALA-Net: Adaptive Lesion-Aware Attention Network for 3D Colorectal Tumor Segmentation. IEEE Transaction of Medical Imaging.

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Jiahong Qian, Shijuan Lu, Yuan Gao,  Yubo Tao, Jun Lin, Hai Lin (2021). An automatic tooth reconstruction method based on multimodal data. Journal of Visualization.

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YanKai Jiang, Lei Zhang, Yiming Li, Xiangyang He, Hanxiao Huang, Keqing Zhu,  Yubo Tao, Hai Lin (2021). DeepNFT: Towards Precise Neurofibrillary Tangle Detection via Improving Multi-scale Feature Fusion and Adversary. IEEE International Conference on Bioinformatics and Biomedicine.

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Team

Current Memberes

PhD Students Masters
Huan Liu (刘欢) Yiming Li (李一鸣)
Yankai Jiang (江彦开) Yinzhao Wang (王银召) }
Han Wang (王涵) …
Lizhen Yang (杨力臻)
Yuan Li (李源)
…

Graduated Students

PhD Students Masters
Bin Zhang (张斌) Nan Ning (宁楠)
Qirui Wang (王琪瑞) Mengyang Kong (孔梦阳)
Yong Zhang (张勇) Deyan Tang (汤德衍)
Xiaowei Mei (梅晓蔚) Xiaobo Luo (罗晓博)
Yuyu Yan (严宇宇) Xia Sun (孙夏)
Jin Xu (徐进) Yuwei Chu (褚玉伟)
Jiahong Qian (钱驾宏) Ke Qu (屈珂)
Zhihui Bai (白志慧) Zengchao He (何增超)
Shihui Zhen (甄世慧) Juntian Chen (陈俊天)
Mingjie Pang (庞铭杰) Yuan Gao (高源)
Xiangyang He (何向阳) Weilun Yu (余伟伦)
… Ming Cheng (程铭)
Hao Ma (马灏)
Shijuan Lu (卢诗娟)
Kaiwei Lin (林凯威)
Chuanchang Chen (陈传长)
Weizhi Luo (罗伟志)
Haoran Dai (戴浩然)
…

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