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Chen Qin

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3 papers

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EAAI Journal 2025 Journal Article

Balancing efficiency and accuracy: Extreme gradient boosting and neural networks for near real-time brain deformation prediction in sports collisions

Emily Yik Kwan Chan
Xiancheng Yu
Chen Qin
Mazdak Ghajari

Rapid head motion during sports collisions can cause traumatic brain injury. Head motion can be measured with instrumented mouthguards and fed into finite element (FE) models to predict brain strain, a measure of brain deformation and injury. Due to the computational cost of FE models, deep neural networks have been developed for near real-time prediction. However, they are not used in pitch-side assessments due to their complexity and reliance on full kinematic data, which cannot be reliably transmitted in real-time. We propose an extreme gradient boosting (XGBoost) model with simple input of two kinematic features. Its accuracy and efficiency were compared with two deep learning models: a multilayer perceptron (MLP) using 20 features, and a convolutional neural network (CNN) using entire kinematics. All models were trained on 1701 rugby impacts collected with mouthguards and simulated using the Imperial brain FE model. The XGBoost model predicted strain in key brain regions, while the deep learning models predicted whole-brain strain distributions. All models showed reasonable accuracy in predicting regional strain, with R2 values 0. 764–0. 851 for XGBoost, 0. 721–0. 876 for MLP, and 0. 744–0. 887 for CNN. XGBoost required orders of magnitude fewer floating-point operations, and it used simple input that can be calculated on mouthguards and reliably transmitted in real-time. This study suggests that different models can be used at different stages of brain injury assessment. We hope that the XGBoost model proposed here will lower the barriers for adopting brain strain combined with instrumented mouthguards for pitch-side assessments from elite to grassroot collision sports.

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AAAI Conference 2021 Conference Paper

Bi-Classifier Determinacy Maximization for Unsupervised Domain Adaptation

Shuang Li
Fangrui Lv
Binhui Xie
Chi Harold Liu
Jian Liang
Chen Qin

Unsupervised domain adaptation challenges the problem of transferring knowledge from a well-labelled source domain to an unlabelled target domain. Recently, adversarial learning with bi-classifier has been proven effective in pushing crossdomain distributions close. Prior approaches typically leverage the disagreement between bi-classifier to learn transferable representations, however, they often neglect the classifier determinacy in the target domain, which could result in a lack of feature discriminability. In this paper, we present a simple yet effective method, namely Bi-Classifier Determinacy Maximization (BCDM), to tackle this problem. Motivated by the observation that target samples cannot always be separated distinctly by the decision boundary, here in the proposed BCDM, we design a novel classifier determinacy disparity (CDD) metric, which formulates classifier discrepancy as the class relevance of distinct target predictions and implicitly introduces constraint on the target feature discriminability. To this end, the BCDM can generate discriminative representations by encouraging target predictive outputs to be consistent and determined, meanwhile, preserve the diversity of predictions in an adversarial manner. Furthermore, the properties of CDD as well as the theoretical guarantees of BCDM’s generalization bound are both elaborated. Extensive experiments show that BCDM compares favorably against the existing state-of-the-art domain adaptation methods.

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YNICL Journal 2017 Journal Article

Brain lesion segmentation through image synthesis and outlier detection

Christopher Bowles
Chen Qin
Ricardo Guerrero
Roger Gunn
Alexander Hammers
David Alexander Dickie
Maria Valdés Hernández
Joanna Wardlaw

Cerebral small vessel disease (SVD) can manifest in a number of ways. Many of these result in hyperintense regions visible on T 2-weighted magnetic resonance (MR) images. The automatic segmentation of these lesions has been the focus of many studies. However, previous methods tended to be limited to certain types of pathology, as a consequence of either restricting the search to the white matter, or by training on an individual pathology. Here we present an unsupervised abnormality detection method which is able to detect abnormally hyperintense regions on FLAIR regardless of the underlying pathology or location. The method uses a combination of image synthesis, Gaussian mixture models and one class support vector machines, and needs only be trained on healthy tissue. We evaluate our method by comparing segmentation results from 127 subjects with SVD with three established methods and report significantly superior performance across a number of metrics.

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