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 • 11 Jul 2023 • Guoyao Deng, Ke Zou, Meng Wang, Xuedong Yuan, Sancong Ying, Huazhu Fu
To achieve this, we employ multiple expert models to extract evidence from the abundant neural network information contained in fMRI images.
no code implementations • 11 Jul 2023 • Guoyao Deng, Ke Zou, Kai Ren, Meng Wang, Xuedong Yuan, Sancong Ying, Huazhu Fu
Recently, Segmenting Anything has taken an important step towards general artificial intelligence.
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.
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.