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查看 Scopus 资料
范 敬凡
光电学院
h-index
1550
引用
20
H-指数
根据储存在 Pure 的刊物以及来自 Scopus 的引用文献数量计算
2010
2025
每年的科研成果
概览
指纹图谱
合作网络
科研成果
(133)
相似学者
(8)
指纹图谱
深入其中 Jingfan Fan 为活跃的研究主题。这些主题标签来自此人的成果。它们共同形成唯一的指纹。
分类
加权
按字母排序
Computer Science
Adversarial Machine Learning
22%
Annotation
12%
Augmented Reality
43%
Automatic Segmentation
28%
Clinical Application
12%
Clinical Data
15%
Contextual Information
14%
Convolutional Network
38%
Convolutional Neural Network
23%
de-noising
23%
Deep Learning
35%
Deformation Field
18%
Depth Information
10%
Discriminator
11%
Error Distance
12%
Experimental Result
100%
feature descriptor
17%
Feature Mapping
12%
Gaussian Mixture Model
12%
Generative Adversarial Networks
17%
Image Analysis
14%
image denoising
13%
Image Segmentation
10%
Image Sequence
33%
Learning Framework
22%
Lesion Segmentation
25%
Local Structure
17%
Mean Algorithm
17%
Motion Compensation
12%
Motion Information
12%
Optical Tracking
14%
Point Cloud
37%
Pose Estimation
21%
Registration Error
20%
Rigid Transformation
21%
Segmentation Method
35%
Siamese Neural Network
17%
Structural Similarity
17%
Temporal Feature
20%
Tracking Object
12%
Tracking System
23%
Traditional Method
12%
Transfer Learning
14%
Transformation Parameter
14%
Tree Search
12%
Tumor Segmentation
17%
U-Net
17%
Ultrasound Image
55%
Vessel Centerline
10%
Vessel Segmentation
38%
Engineering
Augmented Reality
25%
Based Detection Method
8%
Brain Image
11%
Centerline
44%
Component Analysis
10%
Convolutional Neural Network
21%
Coordinate System
19%
Deep Learning
35%
Deformation Field
25%
Demonstrates
64%
Discriminator
8%
Distance Map
10%
Energy Engineering
17%
Error Distance
15%
Experimental Result
64%
Fiducials
15%
Filtration
10%
Gaussian Mixture Model
12%
Ground Truth
10%
Head Pose Estimation
8%
Imaging Systems
11%
Instrument Calibration
16%
Laplacian Pyramid
8%
Local Mean
17%
Loss Function
17%
Medical Image
20%
Metrics
11%
Multichannel
8%
Multiscale
34%
Navigation System
26%
Noise Ratio
10%
Obtains
28%
Optical Axis
10%
Patient Specific
8%
Phase Image
8%
Point Cloud
28%
Prediction Performance
10%
Principal Components
10%
Region of Interest
12%
Registration Accuracy
10%
Registration Method
57%
Reinforcement Learning
8%
Rigid Transformation
18%
Segmentation Method
19%
Set Point
25%
Side Branch
12%
Similarities
20%
State-of-the-Art Method
51%
Tasks
41%
Transfer Learning
21%
