no code implementations • 14 Mar 2023 • Weiyang Jin, Yongpei Zhu, Yuxi Peng
This paper aims to identify various physical models that are suitable for constructing ODE-style generative diffusion models accurately from a mathematical perspective.
no code implementations • 23 Jul 2019 • Yongpei Zhu, Zicong Zhou, Guojun Liao, Kehong Yuan
Accurate registration of medical images is vital for doctor's diagnosis and quantitative analysis.
1 code implementation • 22 Apr 2019 • Yongpei Zhu, Hongwei Fan, Kehong Yuan
The greater the distance, the more sensitive the feature map is to the facial feature unit.
Facial Expression Recognition Facial Expression Recognition (FER) +2
no code implementations • 30 Jan 2019 • Yongpei Zhu, Xuesheng Zhang, Kehong Yuan
2) The strategy of similarity measurement is included three parts(patients' chief complaint, pathology results and medical images).
no code implementations • 6 Jan 2019 • Yang Deng, Yao Sun, Yongpei Zhu, Yue Xu, Qianxi Yang, Shuo Zhang, Mingwang Zhu, Jirang Sun, Weiling Zhao, Xiaobo Zhou, Kehong Yuan
In this paper, we propose a new criterion to evaluate efforts of doctors annotating medical image.
no code implementations • 10 Nov 2018 • Yongpei Zhu, Zicong Zhou, Guojun Liao, Qianxi Yang, Kehong Yuan
In this paper, we use the differential geometric information including JD and CV as image characteristics to measure the differences between different MRI images, which represent local size changes and local rotations of the brain image, and we can use them as one CNN channel with other three modalities (T1-weighted, T1-IR and T2-FLAIR) to get more accurate results of brain segmentation.
no code implementations • 23 Jul 2018 • Yang Deng, Yao Sun, Yongpei Zhu, Shuo Zhang, Mingwang Zhu, Kehong Yuan
It is on the basis of this, we propose a judgement to distinguish data sets that different models are good at.
no code implementations • 19 Jul 2018 • Yang Deng, Yao Sun, Yongpei Zhu, Mingwang Zhu, Wei Han, Kehong Yuan
How to choose appropriate training dataset from limited labeled dataset rather than the whole also has great significance in saving training time.