Greetings! My name is Zhihua Liu (Chinese: 刘志华). I am a Ph.D. student supervised by Prof. Huiyu Zhou at the Biomedical Image Processing Lab (BIPL), School of Computing and Mathematical Sciences, University of Leicester. I am funded by the Graduate Teaching Assistantship (GTA), College of Science and Engineering, University of Leicester.
I am looking for research intern (also visiting student) position for year 2024, please find related links below and feel free to contact / chat! (I am looking forward to your feedback and suggestions!!)
Research Intern Interests: Medical Shape Analysis > Visual Motion Modeling > Other Medical Imaging > Other Vision >>> LLM (I am not a LLM guy. Never trained a LLM before. I do not want to lie things like I am a quick learner and I can learn LLM in days.)
To be honest, I do not have specific salary expectations, but I do have preferences regarding the research topic and quality. If you have time, we can chat this further. It's particularly relevant to my situation, considering that I come from an under-represented research group (I have a "theory" on this called "The LASSO Way ⚽").
Accurate tracking of an anatomical landmark over time has been of high interests for disease assessment such as minimally invasive surgery and tumor radiation therapy. In this paper, we propose a long-short diffeomorphic motion network, which is a multi-task framework with a learnable deformation prior to search for the plausible deformation of landmark.
Considering stateof-the-art technologies and their performance, the purpose of this paper is to provide a comprehensive survey of recently developed deep learning based brain tumor segmentation techniques.
Benefited from deep learning techniques, remarkable progress has been made within the medical image analysis area in recent years. However, it is very challenging to fully utilize the relational information (the relationship between tissues or organs or images) within the deep neural network architecture. Thus in this thesis, we propose two novel solutions to this problem called implicit and explicit deep relational learning. We generalize these two paradigms of deep relational learning into different solutions and evaluate them on various medical image analysis tasks.
In this paper, we propose two perceptual enhancement models, each of which uses a deep enhancement model with a detection perceptor. The detection perceptor provides coherent information in the form of gradients to the enhancement model, guiding the enhancement model to generate patch level visually pleasing images or detection favourable images.
The recognition of human action is widely applied in video surveillance, virtual reality and in some human-computer interaction areas such as user experience designing tasks. Pattern recognition becomes a hot topic in the field of computer vision. In this report, I summarize human behavior recognition problem as a problem of acquiring computing data through motion detection and symbolic acting information. Then extract and understand the behavior of the action features to achieve classification target. On this basis, I review the moving object detection, motion feature extraction and movement characteristics to understand the technical analysis, the correlation method classification, and discuss the difficulties and research directions of this project.
Industrial research and engineering work at JD Logistics, JD.com.Focused on vehicle, bicycle and pedestrian detection from 3D point cloud generated from LiDAR. Also focused on engineering work within point cloud data storge, data retrival, data access authentication development.
CO1104 Computer Architecture
CO3102 Mobile and Web Applications
CO4105 Advanced C++ Programming
CO3002 Analysis and Design of Algorithms
CO3099 Foundations of Cybersecurity
CO1109 Business and Financial Computing
CO7218 Financial Services Information Systems
FS0023 STEM Foundation Year Lab-Physics
CO3091 Computational Intelligence and Software Engineering
I like traveling and photography. Here is my instagram.
I also like sports, especially football ⚽ and table tennis 🏓. I started to receive professional table tennis training from the age of 5, got into the school team, and gave up training in high school because of the college entrance examination.