no code implementations • 8 Jan 2024 • Shuge Lei, Haonan Hu, Dasheng Sun, Huabin Zhang, Kehong Yuan, Jian Dai, Jijun Tang, Yan Tong
This paper focuses on the classification task of breast ultrasound images and researches on the reliability measurement of classification results.
no code implementations • 9 Jun 2022 • Shuang Ge, Kehong Yuan, Maokun Han, Desheng Sun, Huabin Zhang, Qiongyu Ye
Artificial intelligence(AI)-assisted method had received much attention in the risk field such as disease diagnosis.
no code implementations • 22 Apr 2022 • Pingping Dai, Haiming Zhu, Shuang Ge, Ruihan Zhang, Xiang Qian, Xi Li, Kehong Yuan
In this paper, inspired by self-training of semi-supervised learning, we pro? pose a novel approach to solve the lack of annotated data from another angle, called medical image pixel rearrangement (short in MIPR).
no code implementations • 20 Mar 2022 • Pingping Dai, Licong Dong, Ruihan Zhang, Haiming Zhu, Jie Wu, Kehong Yuan
The medical datasets are usually faced with the problem of scarcity and data imbalance.
no code implementations • 19 Jan 2022 • Yingni Wanga, Yunxiao Liua, Licong Dongc, Xuzhou Wua, Huabin Zhangb, Qiongyu Yed, Desheng Sunc, Xiaobo Zhoue, Kehong Yuan
Fetal standard scan plane detection during 2-D mid-pregnancy examinations is a highly complex task, which requires extensive medical knowledge and years of training.
no code implementations • 28 Jul 2021 • Shuang Ge, Qiongyu Ye, Wenquan Xie, Desheng Sun, Huabin Zhang, Xiaobo Zhou, Kehong Yuan
Aim: We proposed a new pipeline to automatically generate AI breast ultrasound screening reports based on ultrasound images, aiming to assist doctors in improving the efficiency of clinical screening and reducing repetitive report writing.
no code implementations • 8 Jan 2021 • YingNi Wang, Shuge Lei, Jian Dai, Kehong Yuan
The implementation of medical AI has always been a problem.
no code implementations • 7 Jan 2021 • Jian Dai, Shuge Lei, Licong Dong, Xiaona Lin, Huabin Zhang, Desheng Sun, Kehong Yuan
Significance: 1) The proposed method makes the target detection model more suitable for diagnosing breast ultrasound images.
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 • 1 Aug 2018 • Yao Sun, Yang Deng, Yue Xu, Shuo Zhang, Mingwang Zhu, Kehong Yuan
Magnetic Resonance Imaging (MRI) is widely used in the pathological and functional studies of the brain, such as epilepsy, tumor diagnosis, etc.
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.