Paradigm free mapping: detection and characterization of single trial fMRI BOLD responses without prior stimulus information

The increased contrast to noise ratio available at Ultrahigh (7T) Magnetic Resonance Imaging (MRI) allows mapping in space and time the brain's response to single trial events with functional MRI (fMRI) based on the Blood Oxygenation Level Dependent (BOLD) contrast. This thesis primarily concerns with the development of techniques to detect and characterize single trial event-related BOLD responses without prior paradigm information, Paradigm Free Mapping, and assess variations in BOLD sensitivity across brain regions at high field fMRI. Based on a linear haemodynamic response model, Paradigm Free Mapping (PFM) techniques rely on the deconvolution of the neuronal-related signal driving the BOLD effect using regularized least squares estimators. The first approach, named PFM, builds on the ridge regression estimator and spatio-temporal t-statistics to detect statistically significant changes in the deconvolved fMRI signal. The second method, Sparse PFM, benefits from subset selection features of the LASSO and Dantzig Selector estimators that automatically detect the single trial BOLD responses by promoting a sparse deconvolution of the signal. The third technique, Multicomponent PFM, exploits further the benefits of sparse estimation to decompose the fMRI signal into a haemodynamical component and a baseline component using the morphological component analysis algorithm. These techniques were evaluated in simulations and experimental fMRI datasets, and the results were compared with well-established fMRI analysis methods. In particular, the methods developed here enabled the detection of single trial BOLD responses to visually-cued and self-paced finger tapping responses without prior information of the events. The potential application of Sparse PFM to identify interictal discharges in idiopathic generalized epilepsy was also investigated. Furthermore, Multicomponent PFM allowed us to extract cardiac and respiratory fluctuations of the signal without the need of physiological monitoring. To sum up, this work demonstrates the feasibility to do single trial fMRI analysis without prior stimulus or physiological information using PFM techniques

Paradigm free mapping: detection and characterization of single trial fMRI BOLD responses without prior stimulus information

The increased contrast to noise ratio available at Ultrahigh (7T) Magnetic Resonance Imaging (MRI) allows mapping in space and time the brain's response to single trial events with functional MRI (fMRI) based on the Blood Oxygenation Level Dependent (BOLD) contrast. This thesis primarily concerns with the development of techniques to detect and characterize single trial event-related BOLD responses without prior paradigm information, Paradigm Free Mapping, and assess variations in BOLD sensitivity across brain regions at high field fMRI. Based on a linear haemodynamic response model, Paradigm Free Mapping (PFM) techniques rely on the deconvolution of the neuronal-related signal driving the BOLD effect using regularized least squares estimators. The first approach, named PFM, builds on the ridge regression estimator and spatio-temporal t-statistics to detect statistically significant changes in the deconvolved fMRI signal. The second method, Sparse PFM, benefits from subset selection features of the LASSO and Dantzig Selector estimators that automatically detect the single trial BOLD responses by promoting a sparse deconvolution of the signal. The third technique, Multicomponent PFM, exploits further the benefits of sparse estimation to decompose the fMRI signal into a haemodynamical component and a baseline component using the morphological component analysis algorithm. These techniques were evaluated in simulations and experimental fMRI datasets, and the results were compared with well-established fMRI analysis methods. In particular, the methods developed here enabled the detection of single trial BOLD responses to visually-cued and self-paced finger tapping responses without prior information of the events. The potential application of Sparse PFM to identify interictal discharges in idiopathic generalized epilepsy was also investigated. Furthermore, Multicomponent PFM allowed us to extract cardiac and respiratory fluctuations of the signal without the need of physiological monitoring. To sum up, this work demonstrates the feasibility to do single trial fMRI analysis without prior stimulus or physiological information using PFM techniques

Learning a Convolutional Neural Network with Additional Information

Department of Electrical EngineeringLearning representations for object recognition and image quality enhancement with the deep convolutional neural network approach has received a great deal of attention in the past several years and recently has gained widespread popularity in the field of computer vision and image processing. Many convolutional neural networks based researches has shown successful results in image processing and computer vision area by feeding only raw images into the convolutional neural network model to learn network parameters. In supervised learning, every input image in our traingset is "a question" and corresponding label or groundtruth is "a correct answer" that we would have quite liked the algorithms have predicted on that image. Even though the CNN models can be skillful enough to solve a problem by learning a large trainingset, we expect that if CNNs were feed with additional information, as well as with images, that would leads learning in a better way, it may perform better. It is like giving helpful hints when teaching a child. This thesis presents fingerprint liveness detection and space-varying deblur methods based on CNN. The first fingerprint liveness detection belongs to a classification problem and the second space-varying deblur belongs to a prediction problem of original sharpen image. Instead of training CNNs with input images only, we provide additional information with which CNNs can learn features in a domain specific, or a problem specific way. Simple additional information that can obtain fairly easily but crucial for a given task is used as an additional input of each CNN. Sweat pore map is used as additional information for fingerprint liveness detection and spatial pixel indices are used as additional information for space-varying deblur. For fingerprint liveness detection, the sweat pore map provides enough hint to allow CNN to learn features for specific regions such as regions right at the pores, regions around the pores, regions in the ridges that do not contain pores. Our fingerprint liveness detection method outperforms the best algorithms of fingerprint liveness detection competition 2013(LivDet2013). For CNN based space-varying debur, the spatial pixel indices provide enough hint to allow CNN to learn filters that have stronger response at specific areas of an image. Our non-stationary lens blur method is the first CNN model that directly outputs the restored image from a blurry image, without any assumption or approximation of block-wise spatially invariant blur. The proposed deblurring method provides the state-of-the-art performances. We established procedures for building training sets from real-world lenses and cameras for restoration of lens blur.ope

Adversarial Attacks and Defenses in Machine Learning-Powered Networks: A Contemporary Survey

Adversarial attacks and defenses in machine learning and deep neural network have been gaining significant attention due to the rapidly growing applications of deep learning in the Internet and relevant scenarios. This survey provides a comprehensive overview of the recent advancements in the field of adversarial attack and defense techniques, with a focus on deep neural network-based classification models. Specifically, we conduct a comprehensive classification of recent adversarial attack methods and state-of-the-art adversarial defense techniques based on attack principles, and present them in visually appealing tables and tree diagrams. This is based on a rigorous evaluation of the existing works, including an analysis of their strengths and limitations. We also categorize the methods into counter-attack detection and robustness enhancement, with a specific focus on regularization-based methods for enhancing robustness. New avenues of attack are also explored, including search-based, decision-based, drop-based, and physical-world attacks, and a hierarchical classification of the latest defense methods is provided, highlighting the challenges of balancing training costs with performance, maintaining clean accuracy, overcoming the effect of gradient masking, and ensuring method transferability. At last, the lessons learned and open challenges are summarized with future research opportunities recommended.Comment: 46 pages, 21 figure