Classification of Multiple Interleaved Human Brain Tasks in Functional Magnetic Resonance Imaging

Author(s):  
Manel Martínez-Ramón ◽  
Vladimir Koltchinskii ◽  
Gregory L. Heileman ◽  
Stefan Posse

Pattern recognition in functional magnetic resource imaging (fMRI) is a novel technique that may lead to a quantity of discovery tools in neuroscience. It is intended to automatically identify differences in distributed neural substrates resulting from cognitive tasks. Previous works in fMRI classification revealed that information is organized in coarse areas in the neural tissues rather than in small neural microstructures. This fact opens a field of study of the functional areas of the brain from the multivariate analysis of the rather coarse images provided by fMRI. Nevertheless, reliable pattern classification is challenging due to the high dimensionality of fMRI data, the small number of available data sets, interindividual differences, and dependence on the acquisition methodology. The application of kernel methods and, in particular, SVMs, to pattern recognition of fMRI is a reasonable approach to deal with these difficulties and has given reasonable results in accuracy and generalization ability. Some of the most relevant fMRI classification studies using SVMs are analyzed in this chapter. All of them were applied in individual subjects using ad hoc techniques to isolate small brain areas in order to reduce the dimensionality of the problem. Some of them included blind techniques for feature selection; others used the previous knowledge of the human brain to isolate the areas in which the information is presumed to lie. Nevertheless, these methods do not explicitly address the dimensionality, small data sets, or cross-subject classification issues. We present an approach to improve multiclass classification across groups of subjects, field strengths, and fMRI methods. We use an approach based on the segmentation of the brain in functional areas using a neuroanatomical atlas, and each map is classified separately using local classifiers. A single multiclass output is applied using an Adaboost aggregation of the classifier’s outputs. This Adaboost combined the region-specific classifiers to achieve improved classification accuracy with respect to conventional techniques without previous ad hoc area or voxel selection.

Author(s):  
Manel Martínez-Ramón ◽  
Vladimir Koltchinskii ◽  
Gregory L. Heileman ◽  
Stefan Posse

Pattern recognition in functional magnetic resource imaging (fMRI) is a novel technique that may lead to a quantity of discovery tools in neuroscience. It is intended to automatically identify differences in distributed neural substrates resulting from cognitive tasks. Previous works in fMRI classification revealed that information is organized in coarse areas in the neural tissues rather than in small neural microstructures. This fact opens a field of study of the functional areas of the brain from the multivariate analysis of the rather coarse images provided by fMRI. Nevertheless, reliable pattern classification is challenging due to the high dimensionality of fMRI data, the small number of available data sets, interindividual differences, and dependence on the acquisition methodology. The application of kernel methods and, in particular, SVMs, to pattern recognition of fMRI is a reasonable approach to deal with these difficulties and has given reasonable results in accuracy and generalization ability. Some of the most relevant fMRI classification studies using SVMs are analyzed in this chapter. All of them were applied in individual subjects using ad hoc techniques to isolate small brain areas in order to reduce the dimensionality of the problem. Some of them included blind techniques for feature selection; others used the previous knowledge of the human brain to isolate the areas in which the information is presumed to lie. Nevertheless, these methods do not explicitly address the dimensionality, small data sets, or cross-subject classification issues. We present an approach to improve multiclass classification across groups of subjects, field strengths, and fMRI methods. We use an approach based on the segmentation of the brain in functional areas using a neuroanatomical atlas, and each map is classified separately using local classifiers. A single multiclass output is applied using an Adaboost aggregation of the classifier’s outputs. This Adaboost combined the region-specific classifiers to achieve improved classification accuracy with respect to conventional techniques without previous ad hoc area or voxel selection.


Author(s):  
Jianping Ju ◽  
Hong Zheng ◽  
Xiaohang Xu ◽  
Zhongyuan Guo ◽  
Zhaohui Zheng ◽  
...  

AbstractAlthough convolutional neural networks have achieved success in the field of image classification, there are still challenges in the field of agricultural product quality sorting such as machine vision-based jujube defects detection. The performance of jujube defect detection mainly depends on the feature extraction and the classifier used. Due to the diversity of the jujube materials and the variability of the testing environment, the traditional method of manually extracting the features often fails to meet the requirements of practical application. In this paper, a jujube sorting model in small data sets based on convolutional neural network and transfer learning is proposed to meet the actual demand of jujube defects detection. Firstly, the original images collected from the actual jujube sorting production line were pre-processed, and the data were augmented to establish a data set of five categories of jujube defects. The original CNN model is then improved by embedding the SE module and using the triplet loss function and the center loss function to replace the softmax loss function. Finally, the depth pre-training model on the ImageNet image data set was used to conduct training on the jujube defects data set, so that the parameters of the pre-training model could fit the parameter distribution of the jujube defects image, and the parameter distribution was transferred to the jujube defects data set to complete the transfer of the model and realize the detection and classification of the jujube defects. The classification results are visualized by heatmap through the analysis of classification accuracy and confusion matrix compared with the comparison models. The experimental results show that the SE-ResNet50-CL model optimizes the fine-grained classification problem of jujube defect recognition, and the test accuracy reaches 94.15%. The model has good stability and high recognition accuracy in complex environments.


2020 ◽  
Vol 98 (Supplement_4) ◽  
pp. 8-9
Author(s):  
Zahra Karimi ◽  
Brian Sullivan ◽  
Mohsen Jafarikia

Abstract Previous studies have shown that the accuracy of Genomic Estimated Breeding Value (GEBV) as a predictor of future performance is higher than the traditional Estimated Breeding Value (EBV). The purpose of this study was to estimate the potential advantage of selection on GEBV for litter size (LS) compared to selection on EBV in the Canadian swine dam line breeds. The study included 236 Landrace and 210 Yorkshire gilts born in 2017 which had their first farrowing after 2017. GEBV and EBV for LS were calculated with data that was available at the end of 2017 (GEBV2017 and EBV2017, respectively). De-regressed EBV for LS in July 2019 (dEBV2019) was used as an adjusted phenotype. The average dEBV2019 for the top 40% of sows based on GEBV2017 was compared to the average dEBV2019 for the top 40% of sows based on EBV2017. The standard error of the estimated difference for each breed was estimated by comparing the average dEBV2019 for repeated random samples of two sets of 40% of the gilts. In comparison to the top 40% ranked based on EBV2017, ranking based on GEBV2017 resulted in an extra 0.45 (±0.29) and 0.37 (±0.25) piglets born per litter in Landrace and Yorkshire replacement gilts, respectively. The estimated Type I errors of the GEBV2017 gain over EBV2017 were 6% and 7% in Landrace and Yorkshire, respectively. Considering selection of both replacement boars and replacement gilts using GEBV instead of EBV can translate into increased annual genetic gain of 0.3 extra piglets per litter, which would more than double the rate of gain observed from typical EBV based selection. The permutation test for validation used in this study appears effective with relatively small data sets and could be applied to other traits, other species and other prediction methods.


Author(s):  
Jungeui Hong ◽  
Elizabeth A. Cudney ◽  
Genichi Taguchi ◽  
Rajesh Jugulum ◽  
Kioumars Paryani ◽  
...  

The Mahalanobis-Taguchi System is a diagnosis and predictive method for analyzing patterns in multivariate cases. The goal of this study is to compare the ability of the Mahalanobis-Taguchi System and a neural network to discriminate using small data sets. We examine the discriminant ability as a function of data set size using an application area where reliable data is publicly available. The study uses the Wisconsin Breast Cancer study with nine attributes and one class.


2018 ◽  
Vol 121 (16) ◽  
Author(s):  
Wei-Chia Chen ◽  
Ammar Tareen ◽  
Justin B. Kinney

2011 ◽  
Vol 19 (2-3) ◽  
pp. 133-145
Author(s):  
Gabriela Turcu ◽  
Ian Foster ◽  
Svetlozar Nestorov

Text analysis tools are nowadays required to process increasingly large corpora which are often organized as small files (abstracts, news articles, etc.). Cloud computing offers a convenient, on-demand, pay-as-you-go computing environment for solving such problems. We investigate provisioning on the Amazon EC2 cloud from the user perspective, attempting to provide a scheduling strategy that is both timely and cost effective. We derive an execution plan using an empirically determined application performance model. A first goal of our performance measurements is to determine an optimal file size for our application to consume. Using the subset-sum first fit heuristic we reshape the input data by merging files in order to match as closely as possible the desired file size. This also speeds up the task of retrieving the results of our application, by having the output be less segmented. Using predictions of the performance of our application based on measurements on small data sets, we devise an execution plan that meets a user specified deadline while minimizing cost.


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