1 code implementation • 13 Sep 2023 • Xianghao Zhan, Qinmei Xu, Yuanning Zheng, Guangming Lu, Olivier Gevaert
This method capitalizes on a small set of accurately labeled training data and leverages ICP-calculated reliability metrics to rectify mislabeled data and outliers within vast quantities of noisy training data.
no code implementations • 8 Jun 2023 • Xianghao Zhan, Jiawei Sun, Yuzhe Liu, Nicholas J. Cecchi, Enora Le Flao, Olivier Gevaert, Michael M. Zeineh, David B. Camarillo
Machine learning head models (MLHMs) are developed to estimate brain deformation for early detection of traumatic brain injury (TBI).
no code implementations • 19 Dec 2022 • Xianghao Zhan, Yuzhe Liu, Nicholas J. Cecchi, Ashlyn A. Callan, Enora Le Flao, Olivier Gevaert, Michael M. Zeineh, Gerald A. Grant, David B. Camarillo
Wearable sensors for measuring head kinematics can be noisy due to imperfect interfaces with the body.
no code implementations • 9 Mar 2022 • Xianghao Zhan, Fanjin Wang, Olivier Gevaert
Drug-induced liver injury (DILI) describes the adverse effects of drugs that damage liver.
no code implementations • 27 Oct 2021 • Xianghao Zhan, Yuzhe Liu, Nicholas J. Cecchi, Olivier Gevaert, Michael M. Zeineh, Gerald A. Grant, David B. Camarillo
The brain dynamics decomposition enables better interpretation of the patterns in brain injury metrics and the sensitivity of brain injury metrics across impact types.
no code implementations • 20 Oct 2021 • Hengyang Wang, Xianghao Zhan, Li Liu, Asif Ullah, Huiyan Li, Han Gao, You Wang, Guang Li
The results show that DRCA improved the classification accuracy on six subjects (p < 0. 05), compared with the baseline models trained only with the source domain data;, while CPSC did not guarantee the accuracy improvement.
no code implementations • 31 Aug 2021 • Xianghao Zhan, Yuzhe Liu, Nicholas J. Cecchi, Olivier Gevaert, Michael M. Zeineh, Gerald A. Grant, David B. Camarillo
To address the computational cost of FEM, the limited strain rate prediction, and the generalizability of MLHMs to on-field datasets, we propose data fusion and transfer learning to develop a series of MLHMs to predict the maximum principal strain (MPS) and maximum principal strain rate (MPSR).
no code implementations • 7 Aug 2021 • Xianghao Zhan, Yiheng Li, Yuzhe Liu, Nicholas J. Cecchi, Olivier Gevaert, Michael M. Zeineh, Gerald A. Grant, David B. Camarillo
However, due to different kinematic characteristics, many brain injury risk estimation models are not generalizable across the variety of impacts that humans may sustain.
no code implementations • 2 Aug 2021 • Li Liu, Xianghao Zhan, Xikai Yang, Xiaoqing Guan, Rumeng Wu, Zhan Wang, Zhiyuan Luo, You Wang, Guang Li
As an effective framework to quantify the prediction reliability, conformal prediction (CP) was developed with the CPKNN (CP with kNN).
no code implementations • 19 Apr 2021 • Xianghao Zhan, Yiheng Li, Yuzhe Liu, Nicholas J. Cecchi, Samuel J. Raymond, Zhou Zhou, Hossein Vahid Alizadeh, Jesse Ruan, Saeed Barbat, Stephen Tiernan, Olivier Gevaert, Michael M. Zeineh, Gerald A. Grant, David B. Camarillo
A random forest classifier with spectral densities of linear acceleration and angular velocity was built to classify head impact types (e. g., football, car crash, mixed martial arts).
1 code implementation • 14 Apr 2021 • Li Liu, Xianghao Zhan, Ziheng Duan, Yi Wu, Rumeng Wu, Xiaoqing Guan, Zhan Wang, You Wang, Guang Li
In this study, we classified different origins of three categories of herbal medicines with different feature extraction methods: manual feature extraction, mathematical transformation, deep learning algorithms.
no code implementations • 9 Feb 2021 • Xianghao Zhan, Yiheng Li, Yuzhe Liu, August G. Domel, Hossein Vahid Alizadeh, Zhou Zhou, Nicholas J. Cecchi, Samuel J. Raymond, Stephen Tiernan, Jesse Ruan, Saeed Barbat, Olivier Gevaert, Michael M. Zeineh, Gerald A. Grant, David B. Camarillo
To better design brain injury criteria, the predictive power of rotational kinematics factors, which are different in 1) the derivative order (angular velocity, angular acceleration, angular jerk), 2) the direction and 3) the power (e. g., square-rooted, squared, cubic) of the angular velocity, were analyzed based on different datasets including laboratory impacts, American football, mixed martial arts (MMA), NHTSA automobile crashworthiness tests and NASCAR crash events.
no code implementations • 5 Feb 2021 • Li Liu, Xianghao Zhan, Rumeng Wu, Xiaoqing Guan, Zhan Wang, Wei zhang, Mert Pilanci, You Wang, Zhiyuan Luo, Guang Li
Furthermore, this study provides a systematic analysis of different augmentation strategies.
no code implementations • 18 Dec 2020 • Xianghao Zhan, Yiheng Li, Yuzhe Liu, August G. Domel, Hossein Vahid Alizadeh, Samuel J. Raymond, Jesse Ruan, Saeed Barbat, Stephen Tiernan, Olivier Gevaert, Michael Zeineh, Gerald Grant, David B. Camarillo
The results show a significant difference in the relationship between BIC and brain strain across datasets, indicating the same BIC value may suggest different brain strain in different head impact types.
no code implementations • 16 Oct 2020 • Xianghao Zhan, Yuzhe Liu, Samuel J. Raymond, Hossein Vahid Alizadeh, August G. Domel, Olivier Gevaert, Michael Zeineh, Gerald Grant, David B. Camarillo
Results: The proposed deep learning head model can calculate the maximum principal strain for every element in the entire brain in less than 0. 001s (with an average root mean squared error of 0. 025, and with a standard deviation of 0. 002 over twenty repeats with random data partition and model initialization).