Browsing by Subject "Deep Learning"
Now showing items 1-20 of 24
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A Comparative Study of Radiomics and Deep-Learning Approaches for Predicting Surgery Outcomes in Early-Stage Non-Small Cell Lung Cancer (NSCLC)
(2022)Purpose: To compare radiomics and deep-learning (DL) methods for predicting NSCLC surgical treatment failure. Methods: A cohort of 83 patients undergoing lobectomy or wedge resection for early-stage NSCLC from our institution ... -
Accelerating Brain DTI and GYN MRI Studies Using Neural Network
(2021)There always exists a demand to accelerate the time-consuming MRI acquisition process. Many methods have been proposed to achieve this goal, including deep learning method which appears to be a robust tool compared ... -
Accelerator Architectures for Deep Learning and Graph Processing
(2020)Deep learning and graph processing are two big-data applications and they are widely applied in many domains. The training of deep learning is essential for inference and has not yet been fully studied. With data forward, ... -
Advancing the Design and Utility of Adversarial Machine Learning Methods
(2021)While significant progress has been made to craft Deep Neural Networks (DNNs) with super-human recognition performance, their reliability and robustness in challenging operating conditions is still a major concern. In this ... -
Deep Automatic Threat Recognition: Considerations for Airport X-Ray Baggage Screening
(2020)Deep learning has made significant progress in recent years, contributing to major advancements in many fields. One such field is automatic threat recognition, where methods based on neural networks have surpassed ... -
Deep Generative Models for Vision, Languages and Graphs
(2019)Deep generative models have achieved remarkable success in modeling various types of data, ranging from vision, languages and graphs etc. They offer flexible and complementary representations for both labeled and unlabeled ... -
Deep Latent-Variable Models for Natural Language Understanding and Generation
(2020)Deep latent-variable models have been widely adopted to model various types of data, due to its ability to: 1) infer rich high-level information from the input data (especially in a low-resource setting); 2) result in a ... -
Deep learning based spectral extrapolation for dual-source, dual-energy x-ray computed tomography.
(Medical physics, 2020-09)<h4>Purpose</h4>Data completion is commonly employed in dual-source, dual-energy computed tomography (CT) when physical or hardware constraints limit the field of view (FoV) covered by one of two imaging chains. Practically, ... -
Deriving Lung Ventilation MAP Directly from Auto Segmented CT Images Using Deep Convolutional Neural Network (CNN)
(2022)Lung cancer has been the most commonly occurring cancer (J. Ferlay, 2018), with the highest fatality rate worldwide. Lung cancer patients undergoing radiation therapy typically experience many side effects. In order to reduce ... -
Development of Deep Learning Models for Deformable Image Registration (DIR) in the Head and Neck Region
(2020)Deformable image registration (DIR) is the process of registering two or more images to a reference image by minimizing local differences across the entire image. DIR is conventionally performed using iterative optimization-based ... -
Dynamic Metamaterials for Far-Infrared Imaging and Spectroscopy
(2019)As early as 1949, it was predicted that a technological gap would form in the far infrared. This so-called ``terahertz gap" is the result of two limitations. On one side, the atomic phenomena giving rise to laser technologies ... -
Evaluating renal lesions using deep-learning based extension of dual-energy FoV in dual-source CT-A retrospective pilot study.
(European journal of radiology, 2021-06)<h4>Purpose</h4>Dual-source (DS) CT, dual-energy (DE) field of view (FoV) is limited to the size of the smaller detector array. The purpose was to establish a deep learning-based approach to DE extrapolation by estimating ... -
Fast MRI Reconstruction using Deep Learning Methods: A Feasibility Study
(2020)As an indispensable imaging modality, magnetic resonance imaging is a non-invasive and radiation-free procedure that could provide high soft-tissue contrast of the patients’ body. However, its time-consuming acquisition ... -
MRI-Based Deep Learning Segmentation and Radiomics of Sarcoma in Mice.
(Tomography (Ann Arbor, Mich.), 2020-03)Small-animal imaging is an essential tool that provides noninvasive, longitudinal insight into novel cancer therapies. However, considerable variability in image analysis techniques can lead to inconsistent results. We have ... -
On the Knowledge Transfer via Pretraining, Distillation and Federated Learning
(2022)Modern machine learning technology based on a revival of deep neural networks has been successfully applied in many pragmatic domains such as computer vision(CV) and natural language processing(NLP). The very standard paradigm ... -
On-board Image Augmentation Using Prior Image and Deep Learning for Image-guided Radiation Therapy
(2019)Cone-beam Computed Tomography (CBCT) has been widely used in image-guided radiation therapy for target localization. 3D CBCT has been developed for localizing static targets, while 4D CBCT has been developed for localizing ... -
Prediction of Bitcoin prices using Twitter Data and Natural Language Processing
(2021-12-16)The influence of social media platforms like Twitter had long been perceived as a bellwether of Bitcoin Prices. This paper aims to investigate if the tweets can be modeled using two different approaches, namely, the Naïve ... -
Realtime Image Processing for Resource Constrained Devices
(2018)With the proliferation of embedded sensors within smartphone and Internet-of-Things devices, applications have programmatic access to more data processing than ever before. At the same time, advances in computer vision and ... -
Security and Robustness in Neuromorphic Computing and Deep Learning
(2020)Machine learning (ML) has been promoting fast in the recent decade. Among many ML algorithms, inspired by biological neural systems, neural networks (NNs) and neuromorphic computing systems (NCSs) achieve state-of-the-art ... -
Statistical Learning of Particle Dispersion in Turbulence and Modeling Turbulence via Deep Learning Techniques
(2021)Turbulence is a complex dynamical system that is strongly high-dimensional, non-linear, non-local and chaotic with a broad range of interacting scales that vary over space and time. It is a common characteristic of fluid ...
