I have been a DPhil student in Computer Science and St-Hilda's College since 2019. Prior to that, I completed an MRes degree with distinction in `Medical Robotics and Image-Guided Intervention' at Imperial College London in 2018, and a BEng degree in Automation at Xi'an Jiaotong University, China in 2017.
My current research involves techniques for deep learning and computer vision, with practical application in medical image segmentation. Some of my other interests are around image registration, depth estimation, stereo matching, gait analysis, and robotics.
I study image segmentation machine learning models around U-Net, LinkNet, PSPNet, FPN, Atrous CNN, Pooling layer design, Attention, label editing, with supervised learning, weakly-supervised learning, semi-supervised learning. My research improves their performance on Ultrasound, MRI and CT medical data.
I have served as a TA in Information Theory at the Mathematical Institute.
RAR−U−Net: a Residual Encoder to Attention Decoder by Residual Connections Framework for Spine Segmentation under Noisy Labels
Ziyang Wang‚ Zhengdong Zhang and Irina Voiculescu
The 28th IEEE International Conference on Image Processing (IEEE − ICIP). September, 2021.