Bio

Zengqiang (John) Yan

PhD candidate, 2016-2020

I joined the Department of Computer Science and Engineering at the Hong Kong University of Science and Technology in Sep. 2016 for PhD study, supervised by Prof. Kwang-Ting (Tim) Cheng.

My research interests include computer vision and deep learning, with specific focus on deep learning based medical image processing.

News

[IEEE TMI, 2019.02] One paper, entitled "Enabling a Single Deep Learning Model for Accurate Gland Instance Segmentation: A Shape-aware Adversarial Learning Framework", has been submitted to IEEE Transactions on Medical Imaging. (under revision)

[IJCV, 2018.09] The co-authored paper, entitled "Describing Upper Body Motions based on the Labanotation for Learning-from-Observation Robots", has been accepted by International Journal of Computer Vision (IJCV).

[IEEE JBHI, 2018.09] The paper, entitled "A Three-stage Deep Learning Model for Accurate Retinal Vessel Segmentation", has been accepted by IEEE Journal of Biomedical and Health Informatics (IEEE JBHI).

[PQE, 2018.09] Finally, I have passed my PhD qualifying exam and become a PhD candidate! 😄 Two years to graduate! 😂

[IEEE JBHI, 2018.08] The revised paper, entitled "A Three-stage Deep Learning Model for Accurate Retinal Vessel Segmentation", has been submitted to IEEE Journal of Biomedical and Health Informatics (under review after major revision).

[IEEE TMM, 2018.07] One paper, entitled "Large-area Depth Recovery for Planar Surfaces in RGB-D Images", has been submitted to IEEE Transactions on Multimedia. (under review)

[MICCAI, 2018.05] The paper, entitled "A Deep Model with Shape-preserving Loss for Gland Instance Segmentation", has been accepted by the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) 2018. See you in Spain! [pdf]

[IEEE TBME, 2018.04] The paper, entitled "Joint Segment-level and Pixel-wise Losses for Deep Learning based Retinal Vessel Segmentation", has been accepted by IEEE Transactions on Biomedical Engineering (IEEE TBME). [pdf]

[IEEE JBHI, 2018.04] One paper, entitled "A Three-stage Deep Learning Model for Accurate Retinal Vessel Segmentation", has been submitted to IEEE Journal of Biomedical and Health Informatics. (under review).

[IEEE TBME, 2018.03] One paper, entitled "Joint Segment-level and Pixel-wise Losses for Deep Learning based Retinal Vessel Segmentation", has been submitted to IEEE Transactions on Biomedical Engineering. (under review after major revision)

[IEEE TMI, 2017.11] The paper, entitled "A Skeletal Similarity Metric for Quality Evaluation of Retinal Vessel Segmentation", has been accepted by IEEE Transactions on Medical Imaging (IEEE TMI). [pdf]

Experience

Internship @MSRA

2015.06 - 2015.12

During my internship at Microsoft Research Asia, I worked with Prof. Katsushi Ikeuchi (IEEE Fellow) on a very interesting topic about humanoid robots. With the help of great colleagues, we developed the learning-from-observation paradigm on two different kinds of robot platforms. The demo was selected to the TechFest 2016 of Microsoft.

Visit @TAMU

2014.12 - 2015.04

During my visit at Texas A&M University, I worked with Prof. Zixiang Xiong (IEEE Fellow) on machine learning and depth image processing. Under the guidance of Prof. Xiong, I developed a large-area depth recovery algorithm for depth enhancement. The paper won the top 10% paper award at the IEEE Workshop on MMSP 2015.

Award

HKUST Postgraduate Scholarship (2016-present)

Award of Excellence, Stars of Tomorrow Internship Program, Microsoft Research Asia 2016

HUAWEI Scholarship (2016)

Top 10% Paper Award, IEEE International Workshop on Multimedia Signal Processing 2015

Student Travel Grant Award, IEEE Signal Processing Society 2015

Selected Publication [All]

Under Revision

Enabling a Single Deep Learning Model for Accurate Gland Instance Segmentation: A Shape-aware Adversarial Learning Framework
Zengqiang Yan, Xin Yang, Kwang-Ting Cheng
IEEE Transactions on Medical Imaging (IEEE TMI), 2019.

Journal

A Three-stage Deep Learning Model for Accurate Retinal Vessel Segmentation
Zengqiang Yan, Xin Yang, Kwang-Ting Cheng
IEEE Journal of Biomedical and Health Informatics (IEEE JBHI), 2018.

Joint Segment-level and Pixel-wise Losses for Deep Learning based Retinal Vessel Segmentation
Zengqiang Yan, Xin Yang, Kwang-Ting Cheng
IEEE Transactions on Biomedical Engineering (IEEE TBME), 2018. [ Github ]

A Skeletal Similarity Metric for Quality Evaluation of Vessel Segmentation
Zengqiang Yan, Xin Yang, Kwang-Ting Cheng
IEEE Transactions on Medical Imaging (IEEE TMI), 2017. [ Github ]

Describing Upper Body Motions based on the Labanotation for Learning-from-Observation Robots
Katsushi Ikeuchi, Zhaoyuan Ma, Zengqiang Yan, Shunsuke Kudoh, Minako Nakamura
International Journal of Computer Vision (IJCV), 2018.

Conference

A Deep Model with Shape-preserving Loss for Gland Instance Segmentation
Zengqiang Yan, Xin Yang, Kwang-Ting Cheng
Proceedings of the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2018.

External Links