[발표] 2015개정 실과 교육과정 ‘가정생활’ 영역 개발 방향

金水暎 著, 「政策學原論」 (법지사, 1992)

Mental disorders such as Autism Spectrum Disorders (ASD) are heterogeneous disorders that are notoriously difficult to diagnose, especially in children. The current psychiatric diagnostic process is based purely on the behavioural observation of symptomology (DSM-5/ICD-10) and may be prone to over-prescribing of drugs due to misdiagnosis. In order to move the field towards more quantitative fashion, we need advanced and scalable machine learning infrastructure that will allow us to identify reliable biomarkers of mental health disorders. In this paper, we propose a framework called ASD-DiagNet for classifying subjects with ASD from healthy subjects by using only fMRI data. We designed and implemented a joint learning procedure using an autoencoder and a single layer perceptron which results in improved quality of extracted features and optimized parameters for the model. Further, we designed and implemented a data augmentation strategy, based on linear interpolation on available feature vectors, that allows us to produce synthetic datasets needed for training of machine learning models. The proposed approach is evaluated on a public dataset provided by Autism Brain Imaging Data Exchange including 1035 subjects coming from 17 different brain imaging centers. Our machine learning model outperforms other existing methods from 10 imaging centers with increase in classification accuracy up to 28% with maximum accuracy of 82%. The machine learning technique presented in this paper, in addition to yielding better quality, gives enormous advantages in terms of execution time (40 minutes vs. 7 hours on other methods). The implemented code is available as GPL license on GitHub portal of our lab (https://github.com/pcdslab/ASD-DiagNet).

/pdf/asd-diagnet-a-hybrid-learning-approach-for-detection-of-4zlg3a1x9y.pdf

ASD-DiagNet: A Hybrid Learning Approach for Detection of Autism Spectrum Disorder Using fMRI Data.

Accurate diagnosis of Autism Spectrum Disorder (ASD) is essential for its management and rehabilitation Neuroimaging techniques that are non-invasive are disease markers and may be leveraged to aid ASD diagnosis Structural and functional neuroimaging techniques provide physicians substantial information about the structure (anatomy and structural connectivity) and function (activity and functional connectivity) of the brain Due to the intricate structure and function of the brain, diagnosing ASD with neuroimaging data without exploiting artificial intelligence (AI) techniques is extremely challenging AI techniques comprise traditional machine learning (ML) approaches and deep learning (DL) techniques Conventional ML methods employ various feature extraction and classification techniques, but in DL, the process of feature extraction and classification is accomplished intelligently and integrally In this paper, studies conducted with the aid of DL networks to distinguish ASD were investigated Rehabilitation tools provided by supporting ASD patients utilizing DL networks were also assessed Finally, we presented important challenges in this automated detection and rehabilitation of ASD

Deep Learning for Neuroimaging-based Diagnosis and Rehabilitation of Autism Spectrum Disorder: A Review

Hi-GCN: A hierarchical graph convolution network for graph embedding learning of brain network and brain disorders prediction.

Quantitative analysis of brain disorders such as Autism Spectrum Disorder (ASD) is an ongoing field of research. Machine learning and deep learning techniques have been playing an important role in automating the diagnosis of brain disorders by extracting discriminative features from the brain data. In this study, we propose a model called Auto-ASD-Network in order to classify subjects with Autism disorder from healthy subjects using only fMRI data. Our model consists of a multilayer perceptron (MLP) with two hidden layers. We use an algorithm called SMOTE for performing data augmentation in order to generate artificial data and avoid overfitting, which helps increase the classification accuracy. We further investigate the discriminative power of features extracted using MLP by feeding them to an SVM classifier. In order to optimize the hyperparameters of SVM, we use a technique called Auto Tune Models (ATM) which searches over the hyperparameter space to find the best values of SVM hyperparameters. Our model achieves more than 70% classification accuracy for 4 fMRI datasets with the highest accuracy of 80%. It improves the performance of SVM by 26%, the stand-alone MLP by 16% and the state of the art method in ASD classification by 14%. The implemented code will be available as GPL license on GitHub portal of our lab (https://github.com/PCDS).

/pdf/auto-asd-network-a-technique-based-on-deep-learning-and-4lj7oi7pid.pdf

Auto-ASD-Network: A Technique Based on Deep Learning and Support Vector Machines for Diagnosing Autism Spectrum Disorder using fMRI Data

Mental disorders such as Autism Spectrum Disorders (ASD) are heterogeneous disorders that are notoriously difficult to diagnose, especially in children. The current psychiatric diagnostic process is based purely on the behavioural observation of symptomology (DSM-5/ICD-10) and may be prone to over-prescribing of drugs due to misdiagnosis. In order to move the field towards more quantitative fashion, we need advanced and scalable machine learning infrastructure that will allow us to identify reliable biomarkers of mental health disorders. In this paper, we propose a framework called ASD-DiagNet for classifying subjects with ASD from healthy subjects by using only fMRI data. We designed and implemented a joint learning procedure using an autoencoder and a single layer perceptron which results in improved quality of extracted features and optimized parameters for the model. Further, we designed and implemented a data augmentation strategy, based on linear interpolation on available feature vectors, that allows us to produce synthetic datasets needed for training of machine learning models. The proposed approach is evaluated on a public dataset provided by Autism Brain Imaging Data Exchange including 1035 subjects coming from 17 different brain imaging centers. Our machine learning model outperforms other state of the art methods from 13 imaging centers with increase in classification accuracy up to 20% with maximum accuracy of 80%. The machine learning technique presented in this paper, in addition to yielding better quality, gives enormous advantages in terms of execution time (40 minutes vs. 6 hours on other methods). The implemented code is available as GPL license on GitHub portal of our lab (this https URL).

ASD-DiagNet: A hybrid learning approach for detection of Autism Spectrum Disorder using fMRI data

Functional magnetic resonance imaging (fMRI) is a non-invasive brain imaging technique, which has been regularly used for studying brain’s functional activities in the past few years. A very well-used measure for capturing functional associations in brain is Pearson’s correlation coefficient. Pearson’s correlation is widely used for constructing functional network and studying dynamic functional connectivity of the brain. These are useful measures for understanding the effects of brain disorders on connectivities among brain regions. The fMRI scanners produce huge number of voxels and using traditional central processing unit (CPU)-based techniques for computing pairwise correlations is very time consuming especially when large number of subjects are being studied. In this paper, we propose a graphics processing unit (GPU)-based algorithm called Fast-GPU-PCC for computing pairwise Pearson’s correlation coefficient. Based on the symmetric property of Pearson’s correlation, this approach returns N ( N − 1 ) / 2 correlation coefficients located at strictly upper triangle part of the correlation matrix. Storing correlations in a one-dimensional array with the order as proposed in this paper is useful for further usage. Our experiments on real and synthetic fMRI data for different number of voxels and varying length of time series show that the proposed approach outperformed state of the art GPU-based techniques as well as the sequential CPU-based versions. We show that Fast-GPU-PCC runs 62 times faster than CPU-based version and about 2 to 3 times faster than two other state of the art GPU-based methods.

https://www.mdpi.com/2571-5135/7/2/11/pdf/1

Fast-GPU-PCC: A GPU-Based Technique to Compute Pairwise Pearson’s Correlation Coefficients for Time Series Data—fMRI Study

Attention deficit hyperactivity disorder (ADHD) is one of the most common brain disorders among children. This disorder is considered as a big threat for public health and causes attention, focus and organizing difficulties for children and even adults. Since the cause of ADHD is not known yet, data mining algorithms are being used to help discover patterns which discriminate healthy from ADHD subjects. Numerous efforts are underway with the goal of developing classification tools for ADHD diagnosis based on functional and structural magnetic resonance imaging data of the brain. In this paper, we used Eros, which is a technique for computing similarity between two multivariate time series along with k-Nearest-Neighbor classifier, to classify healthy vs ADHD children. We designed a model selection scheme called J-Eros which is able to pick the optimum value of k for k-Nearest-Neighbor from the training data. We applied this technique to the public data provided by ADHD-200 Consortium competition and our results show that J-Eros is capable of discriminating healthy from ADHD children such that we outperformed the best results reported by ADHD-200 competition about 20 percent for two datasets. The implemented code is available as GPL license on GitHub portal of our lab at https://github.com/pcdslab/J-Eros.

/pdf/similarity-based-classification-of-adhd-using-singular-value-4gmu9a1gmt.pdf

Taban Eslami

Papers

ASD-DiagNet: A Hybrid Learning Approach for Detection of Autism Spectrum Disorder Using fMRI Data.

Auto-ASD-Network: A Technique Based on Deep Learning and Support Vector Machines for Diagnosing Autism Spectrum Disorder using fMRI Data

ASD-DiagNet: A hybrid learning approach for detection of Autism Spectrum Disorder using fMRI data

Fast-GPU-PCC: A GPU-Based Technique to Compute Pairwise Pearson’s Correlation Coefficients for Time Series Data—fMRI Study

Similarity based classification of ADHD using singular value decomposition