no code implementations • 8 Oct 2024 • Chuansen Peng, Hanning Tang, Zhiguo Wang, Xiaojing Shen
To the best of our knowledge, this is the first work to propose a first-order algorithmic framework for inferring network structures from smooth signals under partial observability, offering both guaranteed linear convergence and practical effectiveness for large-scale networks.
1 code implementation • 18 Aug 2024 • Dawei Dai, Yuanhui Zhang, Long Xu, Qianlan Yang, Xiaojing Shen, Shuyin Xia, Guoyin Wang
In this study, we developed a domain-specific large language-vision assistant (PA-LLaVA) for pathology image understanding.
1 code implementation • 28 May 2024 • Ke Zou, Tian Lin, Zongbo Han, Meng Wang, Xuedong Yuan, Haoyu Chen, Changqing Zhang, Xiaojing Shen, Huazhu Fu
In this study, we propose a novel multi-modality evidential fusion pipeline for eye disease screening.
no code implementations • 10 Apr 2024 • Ke Zou, Yang Bai, Zhihao Chen, Yang Zhou, Yidi Chen, Kai Ren, Meng Wang, Xuedong Yuan, Xiaojing Shen, Huazhu Fu
Medical Report Grounding is pivotal in identifying the most relevant regions in medical images based on a given phrase query, a critical aspect in medical image analysis and radiological diagnosis.
no code implementations • 26 Mar 2024 • Xiaowei Yang, Haiqi Liu, Fanqin Meng, Xiaojing Shen
Directional motion towards a specified destination is a common occurrence in physical processes and human societal activities.
1 code implementation • 17 Mar 2023 • Kai Ren, Ke Zou, Xianjie Liu, Yidi Chen, Xuedong Yuan, Xiaojing Shen, Meng Wang, Huazhu Fu
Our UML has the potential to explore the development of more reliable and explainable medical image analysis models.
1 code implementation • 17 Mar 2023 • Ke Zou, Tian Lin, Xuedong Yuan, Haoyu Chen, Xiaojing Shen, Meng Wang, Huazhu Fu
To address this issue, we introduce a novel multimodality evidential fusion pipeline for eye disease screening, EyeMoSt, which provides a measure of confidence for unimodality and elegantly integrates the multimodality information from a multi-distribution fusion perspective.
no code implementations • 16 Feb 2023 • Ke Zou, Zhihao Chen, Xuedong Yuan, Xiaojing Shen, Meng Wang, Huazhu Fu
We further discuss how they can be estimated in medical imaging.
3 code implementations • 1 Jan 2023 • Ke Zou, Yidi Chen, Ling Huang, Xuedong Yuan, Xiaojing Shen, Meng Wang, Rick Siow Mong Goh, Yong liu, Huazhu Fu
DEviS not only enhances the calibration and robustness of baseline segmentation accuracy but also provides high-efficiency uncertainty estimation for reliable predictions.
no code implementations • 24 Aug 2022 • Xuqi Zhang, Fanqin Meng, Haiqi Liu, Xiaojing Shen, Yunmin Zhu
This paper considers the problem of tracking a large-scale number of group targets.
4 code implementations • 19 Jun 2022 • Ke Zou, Xuedong Yuan, Xiaojing Shen, Meng Wang, Huazhu Fu
In our method, uncertainty is modeled explicitly using subjective logic theory, which treats the predictions of backbone neural network as subjective opinions by parameterizing the class probabilities of the segmentation as a Dirichlet distribution.
no code implementations • 29 Mar 2022 • Yupeng Chen, Zhiguo Wang, Xiaojing Shen
Network topology inference is a fundamental problem in many applications of network science, such as locating the source of fake news, brain connectivity networks detection, etc.
no code implementations • 27 Nov 2021 • Yinchen Shen, Zhiguo Wang, Ruoyu Sun, Xiaojing Shen
Then we propose a feature selection method to reduce the size of the model, based on a new metric which trades off the classification accuracy and privacy preserving.
no code implementations • 29 Sep 2021 • Yinchen Shen, Zhiguo Wang, Ruoyu Sun, Xiaojing Shen
Differential privacy (DP) is an essential technique for privacy-preserving, which works by adding random noise to the data.