ORCID Profile
0000-0002-8901-1472
Current Organisation
University of Technology Sydney
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Publisher: IEEE
Date: 23-05-2022
Publisher: IEEE
Date: 25-09-2022
Publisher: Springer Berlin Heidelberg
Date: 2011
Publisher: Springer International Publishing
Date: 2022
Publisher: Computers, Materials and Continua (Tech Science Press)
Date: 2020
Publisher: Natural Sciences Publishing
Date: 05-2014
DOI: 10.12785/AMIS/080329
Publisher: Frontiers Media SA
Date: 04-03-2022
DOI: 10.3389/FMOLB.2022.822810
Abstract: High-frequency oscillations (HFOs), observed within 80–500 Hz of magnetoencephalography (MEG) data, are putative biomarkers to localize epileptogenic zones that are critical for the success of surgical epilepsy treatment. It is crucial to accurately detect HFOs for improving the surgical outcome of patients with epilepsy. However, in clinical practices, detecting HFOs in MEG signals mainly depends on visual inspection by clinicians, which is very time-consuming, labor-intensive, subjective, and error-prone. To accurately and automatically detect HFOs, machine learning approaches have been developed and have demonstrated the promising results of automated HFO detection. More recently, the transformer-based model has attracted wide attention and achieved state-of-the-art performance on many machine learning tasks. In this paper, we are investigating the suitability of transformer-based models on the detection of HFOs. Specifically, we propose a transformer-based HFO detection framework for biomedical MEG one-dimensional signal data. For signal classification, we develop a transformer-based HFO (TransHFO) classification model. Then, we investigate the relationship between depth of deep learning models and classification performance. The experimental results show that the proposed framework outperforms the state-of-the-art HFO classifiers, increasing classification accuracy by 7%. Furthermore, we find that shallow TransHFO ( 10 layers) outperforms deep TransHFO models (≥10 layers) on most data augmented factors.
Publisher: IEEE
Date: 12-2021
Publisher: Springer International Publishing
Date: 2021
Publisher: IEEE
Date: 18-07-2022
Publisher: Springer International Publishing
Date: 2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2022
Publisher: Springer Singapore
Date: 2019
Publisher: Elsevier BV
Date: 03-2022
DOI: 10.1016/J.JBI.2022.104012
Abstract: The goal of mortality prediction task is to predict the future death risk of patients according to their previous Electronic Healthcare Records (EHR). The main challenge of mortality prediction is how to design an accurate and robust predictive model with sequential, multivariate, sparse and irregular EHR data. In addition, the performance of model may be affected by lack of sufficient information of some patients with rare diseases in EHRs. To address these challenges, we propose a model to fuse Sequential visits and Medical Ontology to predict patients' death risk. SeMO not only learns reasonable embeddings for medical concepts from sequential and irregular visits, but also exploits medical ontology to improve the prediction performance. With integration of multivariate features, SeMO learns robust representations of medical codes, mitigating data insufficiency and insightful sequential dependencies among patient's visits. Experimental results on real world datasets prove that the proposed SeMO improves the prediction performance compared with the baseline approaches. Our model achieves an precision of up to 0.975. Compared with RNN, the precision has been improved up to 2.204%.
Publisher: IEEE
Date: 12-2010
Publisher: Computers, Materials and Continua (Tech Science Press)
Date: 2019
Publisher: Springer Science and Business Media LLC
Date: 28-04-2022
Publisher: IEEE
Date: 13-10-2023
Publisher: IEEE
Date: 11-2007
Publisher: Springer International Publishing
Date: 2022
Publisher: Wiley
Date: 28-04-2020
DOI: 10.1002/CPE.5775
Abstract: The precise named entity recognition (NER) is a key component in Chinese clinical natural language processing. Although clinical NER systems have attracted widespread attention and been studied for decades, the latest NER research usually relies on a shallow text representation with one‐layer neural encoding, which fails to capture deep features and limits its performance improvement. To capture more features and encode the clinical text efficiently, we propose a deep stacked neural network for Chinese clinical NER. The neural network stacks two bidirectional long‐short term memory and gated recurrent unit layers to encode the text twice, followed by a conditional random fields (CRF) layer to recognize named entities in Chinese clinical text. Extensive empirical results on three real‐world datasets demonstrate that the proposed method significantly outperforms six state‐of‐the‐art NER methods. Especially compared with the conventional CRF model, our method has at least 3.75% F 1 ‐score improvement on these public datasets.
Publisher: ACM
Date: 04-03-2022
Publisher: IEEE
Date: 12-2012
Publisher: IEEE
Date: 12-2008
Publisher: Elsevier BV
Date: 03-2023
Publisher: Frontiers Media SA
Date: 09-09-2022
DOI: 10.3389/FNINF.2022.771965
Abstract: Magnetoencephalography is a noninvasive neuromagnetic technology to record epileptic activities for the pre-operative localization of epileptogenic zones, which has received increasing attention in the diagnosis and surgery of epilepsy. As reported by recent studies, pathological high frequency oscillations (HFOs), when utilized as a biomarker to localize the epileptogenic zones, result in a significant reduction in seizure frequency, even seizure elimination in around 80% of cases. Thus, objective, rapid, and automatic detection and recommendation of HFOs are highly desirable for clinicians to alleviate the burden of reviewing a large amount of MEG data from a given patient. Despite the advantage, the performance of existing HFOs rarely satisfies the clinical requirement. Consequently, no HFOs have been successfully applied to real clinical applications so far. In this work, we propose a multi-head self-attention-based detector for recommendation, termed MSADR, to detect and recommend HFO signals. Taking advantage of the state-of-the-art multi-head self-attention mechanism in deep learning, the proposed MSADR achieves a more superior accuracy of 88.6% than peer machine learning models in both detection and recommendation tasks. In addition, the robustness of MSADR is also extensively assessed with various ablation tests, results of which further demonstrate the effectiveness and generalizability of the proposed approach.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: Frontiers Media SA
Date: 08-03-2023
DOI: 10.3389/FMOLB.2023.1136071
Abstract: In intensive care units (ICUs), mortality prediction is performed by combining information from these two sources of ICU patients by monitoring patient health. Respectively, time series data generated from each patient admission to the ICU and clinical records consisting of physician diagnostic summaries. However, existing mortality prediction studies mainly cascade the multimodal features of time series data and clinical records for prediction, ignoring thecross-modal correlation between the underlying features in different modal data. To address theseissues, we propose a multimodal fusion model for mortality prediction that jointly models patients’ time-series data as well as clinical records. We apply a fine-tuned Bert model (Bio-Bert) to the patient’s clinical record to generate a holistic embedding of the text part, which is then combined with the output of an LSTM model encoding the patient’s time-series data to extract valid features. The global contextual information of each modal data is extracted using the improved fusion module to capture the correlation between different modal data. Furthermore, the improved fusion module can be easily added to the fusion features of any unimodal network and utilize existing pre-trained unimodal model weights. We use a real dataset containing 18904 ICU patients to train and evaluate our model, and the research results show that the representations obtained by themodel can achieve better prediction accuracy compared to the baseline.
Publisher: Springer International Publishing
Date: 2019
Publisher: China Science Publishing & Media Ltd.
Date: 09-09-2011
Publisher: IEEE
Date: 07-2019
Publisher: IEEE
Date: 11-2019
Publisher: International Academy Publishing (IAP)
Date: 10-2012
Publisher: Springer Science and Business Media LLC
Date: 08-08-2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2019
Publisher: Springer Nature Switzerland
Date: 2023
Publisher: Springer International Publishing
Date: 2022
Publisher: Elsevier BV
Date: 12-2022
DOI: 10.1016/J.COMPBIOMED.2022.106294
Abstract: Brain tissue of Magnetic Resonance Imaging is precisely segmented and quantified, which aids in the diagnosis of neurological diseases such as epilepsy, Alzheimer's, and multiple sclerosis. Recently, UNet-like architectures are widely used for medical image segmentation, which achieved promising performance by using the skip connection to fuse the low-level and high-level information. However, In the process of integrating the low-level and high-level information, the non-object information (noise) will be added, which reduces the accuracy of medical image segmentation. Likewise, the same problem also exists in the residual unit. Since the output and input of the residual unit are fused, the non-object information (noise) of the input of the residual unit will be in the integration. To address this challenging problem, in this paper we propose a Purified Residual U-net for the segmentation of brain tissue. This model encodes the image to obtain deep semantic information and purifies the information of low-level features and the residual unit from the image, and acquires the result through a decoder at last. We use the Dilated Pyramid Separate Block (DPSB) as the first block to purify the features for each layer in the encoder without the first layer, which expands the receptive field of the convolution kernel with only a few parameters added. In the first layer, we have explored the best performance achieved with DPB. We find the most non-object information (noise) in the initial image, so it is good for the accuracy to exchange the information to the max degree. We have conducted experiments with the widely used IBSR-18 dataset composed of T-1 weighted MRI volumes from 18 subjects. The results show that compared with some of the cutting-edge methods, our method enhances segmentation performance with the mean dice score reaching 91.093% and the mean Hausdorff distance decreasing to 3.2606.
Publisher: Springer International Publishing
Date: 2015
Publisher: Springer Berlin Heidelberg
Date: 2009
Publisher: IEEE
Date: 11-2021
Publisher: Frontiers Media SA
Date: 27-11-2020
DOI: 10.3389/FPHYS.2020.604764
Abstract: As a long-standing chronic disease, Temporal Lobe Epilepsy (TLE), resulting from abnormal discharges of neurons and characterized by recurrent episodic central nervous system dysfunctions, has affected more than 70% of drug-resistant epilepsy patients across the world. As the etiology and clinical symptoms are complicated, differential diagnosis of TLE mainly relies on experienced clinicians, and specific diagnostic biomarkers remain unclear. Though great effort has been made regarding the genetics, pathology, and neuroimaging of TLE, an accurate and effective diagnosis of TLE, especially the TLE subtypes, remains an open problem. It is of a great importance to explore the brain network of TLE, since it can provide the basis for diagnoses and treatments of TLE. To this end, in this paper, we proposed a multi-head self-attention model (MSAM). By integrating the self-attention mechanism and multilayer perceptron method, the MSAM offers a promising tool to enhance the classification of TLE subtypes. In comparison with other approaches, including convolutional neural network (CNN), support vector machine (SVM), and random forest (RF), experimental results on our collected MEG dataset show that the MSAM achieves a supreme performance of 83.6% on accuracy, 90.9% on recall, 90.7% on precision, and 83.4% on F1-score, which outperforms its counterparts. Furthermore, effectiveness of varying head numbers of multi-head self-attention is assessed, which helps select the optimal number of multi-head. The self-attention aspect learns the weights of different signal locations which can effectively improve classification accuracy. In addition, the robustness of MSAM is extensively assessed with various ablation tests, which demonstrates the effectiveness and generalizability of the proposed approach.
Publisher: Hindawi Limited
Date: 31-01-2020
DOI: 10.1155/2020/1357853
Abstract: Autism spectrum disorder (ASD) is a developmental disorder that impacts more than 1.6% of children aged 8 across the United States. It is characterized by impairments in social interaction and communication, as well as by a restricted repertoire of activity and interests. The current standardized clinical diagnosis of ASD remains to be a subjective diagnosis, mainly relying on behavior-based tests. However, the diagnostic process for ASD is not only time consuming, but also costly, causing a tremendous financial burden for patients’ families. Therefore, automated diagnosis approaches have been an attractive solution for earlier identification of ASD. In this work, we set to develop a deep learning model for automated diagnosis of ASD. Specifically, a multichannel deep attention neural network (DANN) was proposed by integrating multiple layers of neural networks, attention mechanism, and feature fusion to capture the interrelationships in multimodality data. We evaluated the proposed multichannel DANN model on the Autism Brain Imaging Data Exchange (ABIDE) repository with 809 subjects (408 ASD patients and 401 typical development controls). Our model achieved a state-of-the-art accuracy of 0.732 on ASD classification by integrating three scales of brain functional connectomes and personal characteristic data, outperforming multiple peer machine learning models in a k -fold cross validation experiment. Additional k -fold and leave-one-site-out cross validation were conducted to test the generalizability and robustness of the proposed multichannel DANN model. The results show promise for deep learning models to aid the future automated clinical diagnosis of ASD.
Publisher: Springer International Publishing
Date: 2019
Publisher: Frontiers Media SA
Date: 10-08-2022
DOI: 10.3389/FMOLB.2022.931688
Abstract: In recent years, the Burden of Amplitudes and Epileptiform Discharges (BASED) score has been used as a reliable, accurate, and feasible electroencephalogram (EEG) grading scale for infantile spasms. However, manual EEG annotation is, in general, very time-consuming, and BASED scoring is no exception. Convolutional neural networks (CNNs) have proven their great potential in many EEG classification problems. However, very few research studies have focused on the use of CNNs for BASED scoring, a challenging but vital task in the diagnosis and treatment of infantile spasms. This study proposes an automatic BASED scoring framework using EEG and a deep CNN. The feasibility of using CNN for automatic BASED scoring was investigated in 36 patients with infantile spasms by annotating their long-term EEG data with four levels of the BASED score (scores 5, 4, 3, and ≤2). In the validation set, the accuracy was 96.9% by applying a multi-layer CNN to classify the EEG data as a 4-label problem. The extensive experiments have demonstrated that our proposed approach offers high accuracy and, hence, is an important step toward an automatic BASED scoring algorithm. To the best of our knowledge, this is the first attempt to use a CNN to construct a BASED-based scoring model.
Publisher: Springer International Publishing
Date: 2022
Publisher: Elsevier BV
Date: 09-2023
Publisher: IEEE
Date: 18-07-2022
Publisher: IEEE
Date: 11-2011
Publisher: American Scientific Publishers
Date: 30-04-2012
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
No related grants have been discovered for Xueping Peng.