307 research outputs found
Image Classification with CNN-based Fisher Vector Coding
Fisher vector coding methods have been demonstrated to be effective for image classification. With the help of convolutional neural networks (CNN), several Fisher vector coding methods have shown state-of-the-art performance by adopting the activations of a single fully-connected layer as region features. These methods generally exploit a diagonal Gaussian mixture model (GMM) to describe the generative process of region features. However, it is difficult to model the complex distribution of high-dimensional feature space with a limited number of Gaussians obtained by unsupervised learning. Simply increasing the number of Gaussians turns out to be inefficient and computationally impractical.
To address this issue, we re-interpret a pre-trained CNN as the probabilistic discriminative model, and present a CNN based Fisher vector coding method, termed CNN-FVC. Specifically, activations of the intermediate fully-connected and output soft-max layers are exploited to derive the posteriors, mean and covariance parameters for Fisher vector coding implicitly. To further improve the efficiency, we convert the pre-trained CNN to a fully convolutional one to extract the region features. Extensive experiments have been conducted on two standard scene benchmarks (i.e. SUN397 and MIT67) to evaluate the effectiveness of the proposed method. Classification accuracies of 60.7% and 82.1% are achieved on the SUN397 and MIT67 benchmarks respectively, outperforming previous state-of-the-art approaches. Furthermore, the method is complementary to GMM-FVC methods, allowing a simple fusion scheme to further improve performance to 61.1% and 83.1% respectively
Integrating External Controls by Regression Calibration for Genome-Wide Association Study
Genome-wide association studies (GWAS) have successfully revealed many disease-associated genetic variants. For a case-control study, the adequate power of an association test can be achieved with a large sample size, although genotyping large samples is expensive. A cost-effective strategy to boost power is to integrate external control samples with publicly available genotyped data. However, the naive integration of external controls may inflate the type I error rates if ignoring the systematic differences (batch effect) between studies, such as the differences in sequencing platforms, genotype-calling procedures, population stratification, and so forth. To account for the batch effect, we propose an approach by integrating External Controls into the Association Test by Regression Calibration (iECAT-RC) in case-control association studies. Extensive simulation studies show that iECAT-RC not only can control type I error rates but also can boost statistical power in all models. We also apply iECAT-RC to the UK Biobank data for M72 Fibroblastic disorders by considering genotype calling as the batch effect. Four SNPs associated with fibroblastic disorders have been detected by iECAT-RC and the other two comparison methods, iECAT-Score and Internal. However, our method has a higher probability of identifying these significant SNPs in the scenario of an unbalanced case-control association study
Driving Style Recognition Based on Electroencephalography Data From a Simulated Driving Experiment
Driving style is a very important indicator and a crucial measurement of a driver's performance and ability to drive in a safe and protective manner. A dangerous driving style would possibly result in dangerous behaviors. If the driving styles can be recognized by some appropriate classification methods, much attention could be paid to the drivers with dangerous driving styles. The driving style recognition module can be integrated into the advanced driving assistance system (ADAS), which integrates different modules to improve driving automation, safety and comfort, and then the driving safety could be enhanced by pre-warning the drivers or adjusting the vehicle's controlling parameters when the dangerous driving style is detected. In most previous studies, driver's questionnaire data and vehicle's objective driving data were utilized to recognize driving styles. And promising results were obtained. However, these methods were indirect or subjective in driving style evaluation. In this paper a method based on objective driving data and electroencephalography (EEG) data was presented to classify driving styles. A simulated driving system was constructed and the EEG data and the objective driving data were collected synchronously during the simulated driving. The driving style of each participant was classified by clustering the driving data via K-means. Then the EEG data was denoised and the amplitude and the Power Spectral Density (PSD) of four frequency bands were extracted as the EEG features by Fast Fourier transform and Welch. Finally, the EEG features, combined with the classification results of the driving data were used to train a Support Vector Machine (SVM) model and a leave-one-subject-out cross validation was utilized to evaluate the performance. The SVM classification accuracy was about 80.0%. Conservative drivers showed higher PSDs in the parietal and occipital areas in the alpha and beta bands, aggressive drivers showed higher PSD in the temporal area in the delta and theta bands. These results imply that different driving styles were related with different driving strategies and mental states and suggest the feasibility of driving style recognition from EEG patterns
Regional association of pCASL-MRI with FDG-PET and PiB-PET in people at risk for autosomal dominant Alzheimer's disease.
Autosomal dominant Alzheimer's disease (ADAD) is a small subset of Alzheimer's disease that is genetically determined with 100% penetrance. It provides a valuable window into studying the course of pathologic processes that leads to dementia. Arterial spin labeling (ASL) MRI is a potential AD imaging marker that non-invasively measures cerebral perfusion. In this study, we investigated the relationship of cerebral blood flow measured by pseudo-continuous ASL (pCASL) MRI with measures of cerebral metabolism (FDG PET) and amyloid deposition (Pittsburgh Compound B (PiB) PET). Thirty-one participants at risk for ADAD (age 39 ± 13 years, 19 females) were recruited into this study, and 21 of them received both MRI and FDG and PiB PET scans. Considerable variability was observed in regional correlations between ASL-CBF and FDG across subjects. Both regional hypo-perfusion and hypo-metabolism were associated with amyloid deposition. Cross-sectional analyses of each biomarker as a function of the estimated years to expected dementia diagnosis indicated an inverse relationship of both perfusion and glucose metabolism with amyloid deposition during AD development. These findings indicate that neurovascular dysfunction is associated with amyloid pathology, and also indicate that ASL CBF may serve as a sensitive early biomarker for AD. The direct comparison among the three biomarkers provides complementary information for understanding the pathophysiological process of AD
Improved language identification using deep bottleneck network
Effective representation plays an important role in automatic spoken language identification (LID). Recently, several representations that employ a pre-trained deep neural network (DNN) as the front-end feature extractor, have achieved state-of-the-art performance. However the performance is still far from satisfactory for dialect and short-duration utterance identification tasks, due to the deficiency of existing representations. To address this issue, this paper proposes the improved representations to exploit the information extracted from different layers of the DNN structure. This is conceptually motivated by regarding the DNN as a bridge between low-level acoustic input and high-level phonetic output features. Specifically, we employ deep bottleneck network (DBN), a DNN with an internal bottleneck layer acting as a feature extractor. We extract representations from two layers of this single network, i.e. DBN-TopLayer and DBN-MidLayer. Evaluations on the NIST LRE2009 dataset, as well as the more specific dialect recognition task, show that each representation can achieve an incremental performance gain. Furthermore, a simple fusion of the representations is shown to exceed current state-of-the-art performance
Three-dimensional echo-shifted EPI with simultaneous blip-up and blip-down acquisitions for correcting geometric distortion
Purpose: Echo-planar imaging (EPI) with blip-up/down acquisition (BUDA) can
provide high-quality images with minimal distortions by using two readout
trains with opposing phase-encoding gradients. Because of the need for two
separate acquisitions, BUDA doubles the scan time and degrades the temporal
resolution when compared to single-shot EPI, presenting a major challenge for
many applications, particularly functional MRI (fMRI). This study aims at
overcoming this challenge by developing an echo-shifted EPI BUDA (esEPI-BUDA)
technique to acquire both blip-up and blip-down datasets in a single shot.
Methods: A three-dimensional (3D) esEPI-BUDA pulse sequence was designed by
using an echo-shifting strategy to produce two EPI readout trains. These
readout trains produced a pair of k-space datasets whose k-space trajectories
were interleaved with opposite phase-encoding gradient directions. The two
k-space datasets were separately reconstructed using a 3D SENSE algorithm, from
which time-resolved B0-field maps were derived using TOPUP in FSL and then
input into a forward model of joint parallel imaging reconstruction to correct
for geometric distortion. In addition, Hankel structured low-rank constraint
was incorporated into the reconstruction framework to improve image quality by
mitigating the phase errors between the two interleaved k-space datasets.
Results: The 3D esEPI-BUDA technique was demonstrated in a phantom and an fMRI
study on healthy human subjects. Geometric distortions were effectively
corrected in both phantom and human brain images. In the fMRI study, the visual
activation volumes and their BOLD responses were comparable to those from
conventional 3D echo-planar images. Conclusion: The improved imaging efficiency
and dynamic distortion correction capability afforded by 3D esEPI-BUDA are
expected to benefit many EPI applications.Comment: 8 figures, peer-reviewed journal pape
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