About Me

I received a BSc (2009) degree from China Agricultural University (CAU), Beijing, China and an MSc (2012) degree in Signal Processing from Xidian University, Xi'an, China. I'm currently a PhD candidate at the Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen, the Netherlands. My current research is about brain-inspired computer vision, including computational visual models of feature and object recognition; medical image and data analysis; machine learning algorithms such as deep learning. Specifically, I focus on techniques and extend methods in the field of automatic computer-aided diagnosis system for assisting population based glaucoma screening.

    Awards
  • Won the scholarship of China Scholarship Council (2012-2016), in University of Groningen
  • Won the second prize Scholarship of 2009-2012, in Xidian University
  • Won the progress prize and the second prize Scholarship of 2005-2009, in CAU
  • Won the second prize of Campus Electronic Design Contest, in CAU.

Experience

Medical Image Analysis

Retina fundus image analysis for assisting glaucoma screening

Glaucoma is the second-leading cause of blindness. It damages the optic nerve irreversibly and has no early symptoms. The most common clinical measure is the cup-to-disc ratio. In collaboration with the Department of Ophthalmology of the University Medical Center Groningen, we develop more comprehensive methods to be used for large-scale glaucoma screening of the population.

Medical Image Analysis

Serrated patterns analysis in direct immunofluorescence images

The classification of serrated patterns in direct immunofluorescence images is used for the diagnosis of epidermolysis bullosa acquisita, a subepidermal autoimmune blistering disease of the skin. Together with the Department of Dermatology of the UMCG, we developed an automatic method to classify serrated patterns as u- or n-shaped.

Machine Learning

Brain-inspired computer vision modle

Trainble filters and deep learning etc.

Object Detection

Recognition of architectural and electrical symbols

The automatic recognition of symbols can be used to automatically convert scanned drawings into digital representations compatible with computer aided design software. We propose a novel approach to automatically recognize architectural and electrical symbols.

Education

PhD student 2012-Present

University of Groningen

My research is about brain-inspired computer vision, including computational visual models of feature and object recognition; medical image and data analysis; machine learning algorithms such as deep learning. Specifically, I focus on techniques and extend methods in the field of automatic computer-aided diagnosis system for assisting population based glaucoma screening.

Summer school etc. 2012-2016

University of Groningen

Attended several courses within the MSc of Computer Science at the University of Groningen: Neural Networks and Computational Intelligence, Image Processing, Pattern Recognition. Attended the International Computer Vision Summer School (ICVSS), Italy, 2013.

Master Degree - 2012

Xidian University

Computer Science Technologies (Signal and Information Processing)

Bachelor Degree - 2009

China Agricultural University

Electronic Information Engineering

Main conferences and events attended

Jan 2017

Computer Aided Medical Diagnostics (CAMED),Las Palmas, Gran Canaria

Presented the talk: "Automatic optic disc and cup segmentation in retinal fundus images for assisting glaucoma screening"

Oct 2015

8th GI Conference on Autonomous Systems, Cala Millor, Spain

Presented the talk and paper: "Automatic Optic Disk Localization and Diameter Estimation in Retinal Fundus Images"

Sep 2015

Computer Analysis of Images and Patterns: 16th International Conference (CAIP), Valletta, Malta

Presented the talk and paper:"Recognition of architectural and electrical symbols by COSFIRE filters with inhibition"

2013-2016

Many International Workshops in Allersmaborg, Groningen, the Netherlands

My Projects

  • All
  • Medical Image Analysis
  • Brain-inspired computer vision
  • Machine learning

Publications

[1] Jiapan Guo, Chenyu Shi, George Azzopardi, Nomdo M. Jansonius, and Nicolai Petkov. Automatic optic disc and cup segmentation in retinal fundus images for assisting glaucoma screening. In preparation. [ bib ]
[2] Chenyu Shi, Joost M. Meijer, Jiapan Guo, George Azzopardi, Marcel F. Jonkman, and Nicolai Petkov. Localization of u-serrated patterns in direct immunofluorescence images by inhibition-augmented COSFIRE filters. In preparation. [ bib ]
[3] Jiapan Guo, Chenyu Shi, George Azzopardi, and Nicolai Petkov. Inhibition-augmented trainable COSFIRE filters for keypoint detection and object recognition. Machine Vision and Applications, 27(8):1197-1211, 2016. [ bib ]
[4] Jiapan Guo, Chenyu Shi, George Azzopardi, and Nicolai Petkov. An inhibition-augmented COSFIRE model of shape-selective neurons. IBM journal special issue on Computational Neuroscience, Accepted. [ bib ]
[5] Jiapan Guo, Chenyu Shi, George Azzopardi, and Nicolai Petkov. Recognition of architectural and electrical symbols by COSFIRE filters with inhibition. In Computer Analysis of Images and Patterns, volume 9257 of Lecture Notes in Computer Science, pages 348-358. Springer International Publishing, 2015. [ bib ]
[6] Chenyu Shi, Jiapan Guo, George Azzopardi, Joost M. Meijer, Marcel F. Jonkman, and Nicolai Petkov. Automatic differentiation of u- and n-serrated patterns in direct immunofluorescence images. In Computer Analysis of Images and Patterns (CAIP 2015), volume 9256 of Lecture Notes in Computer Science, pages 513-521, 2015. [ bib ]
[7] Chenyu Shi, Joost M. Meijer, Jiapan Guo, George Azzopardi, Marcel F. Jonkman, and Nicolai Petkov. Automatic classification of serrated patterns in direct immunofluorescence images. In 8th GI Conference on Autonomous Systems, volume 842, pages 61-69, 2015. [ bib ]
[8] Jiapan Guo, Chenyu Shi, Nomdo M. Jansonius, and Nicolai Petkov. Automatic Optic Disk Localization and Diameter Estimation in Retinal Fundus Images. In 8th GI Conference on Autonomous Systems, volume 842, pages 70-79, 2015. [ bib ]