Browsing by Subject "deep learning"
Now showing items 1-20 of 29
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A Comprehensive Framework for Adaptive Optics Scanning Light Ophthalmoscope Image Analysis
(2019)Diagnosis, prognosis, and treatment of many ocular and neurodegenerative diseases, including achromatopsia (ACHM), require the visualization of microscopic structures in the eye. The development of adaptive optics ophthalmic ... -
A Deep-Learning Method of Automatic VMAT Planning via MLC Dynamic Sequence Prediction (AVP-DSP) Using 3D Dose Prediction: A Feasibility Study of Prostate Radiotherapy Application
(2020)Introduction: VMAT treatment planning requires time-consuming DVH-based inverse optimization process, which impedes its application in time-sensitive situations. This work aims to develop a deep-learning based algorithm, ... -
A Deep-Learning-based Multi-segment VMAT Plan Generation Algorithm from Patient Anatomy for Prostate Simultaneous Integrated Boost (SIB) Cases
(2021)Introduction: Several studies have realized fluence-map-prediction-based DL IMRT planning algorithms. However, DL-based VMAT planning remains unsolved. A main difficult in DL-based VMAT planning is how to generate leaf sequences ... -
A Radiomics-Incorporated Deep Ensemble Learning Model for Multi-Parametric MRI-based Glioma Segmentation
(2023)AbstractPurpose: To develop a deep ensemble learning model with a radiomics spatial encoding execution for improved glioma segmentation accuracy using multi-parametric MRI (mp-MRI). Materials/Methods: This radiomics-incorporated ... -
Applications of Deep Learning, Machine Learning, and Remote Sensing to Improving Air Quality and Solar Energy Production
(2021)Exposure to higher PM2.5 can lead to increased risks of mortality; however, the spatial concentrations of PM2.5 are not well characterized, even in megacities, due to the sparseness of regulatory air quality monitoring (AQM) ... -
Cone Beam Computed Tomography Image Quality Augmentation using Novel Deep Learning Networks
(2019)Purpose: Cone beam computed tomography (CBCT) plays an important role in image guidance for interventional radiology and radiation therapy by providing 3D volumetric images of the patient. However, CBCT suffers from relatively ... -
Contour interpolation by deep learning approach.
(Journal of medical imaging (Bellingham, Wash.), 2022-11)<h4>Purpose</h4>Contour interpolation is an important tool for expediting manual segmentation of anatomical structures. The process allows users to manually contour on discontinuous slices and then automatically fill in ... -
Data Driven Style Transfer for Remote Sensing Applications
(2022)Recent recognition models for remote sensing data (e.g., infrared cameras) are based upon machine learning models such as deep neural networks (DNNs) and typically require large quantities of labeled training data. However, ... -
Deep Generative Models for Image Representation Learning
(2018)Recently there has been increasing interest in developing generative models of data, offering the promise of learning based on the often vast quantity of unlabeled data. With such learning, one typically seeks to build rich, ... -
Deep Generative Models for Vision and Language Intelligence
(2018)Deep generative models have achieved tremendous success in recent years, with applications in various tasks involving vision and language intelligence. In this dissertation, I will mainly discuss the contributions that I ... -
Deep Learning for Automatic Real-time Pulmonary Nodule Detection and Quantitative Analysis
(2019)Purpose: To develop a novel computer-aided diagnosis (CAD) pulmonary nodule detection system that can not only perform real-time detection but also characterize quantitative nodule information based on deep learning ... -
Deep Learning Method for Partial Differential Equations and Optimal Problems
(2023)Scientific computing problems in high dimensions are difficult to solve with traditional methods due to the curse of dimensionality. The recently fast developing machine learning techniques provide us a promising way to ... -
Development of an eXtended Modular ANthropomorphic (XMAN) phantom for Imaging and Treatment Optimization in Radiotherapy
(2021)Developing new technologies for clinical usage requires systematic and rigorous validation and optimization for various patient scenarios to ensure robustness and accuracy. Clinical trials on real patients are always considered ... -
Enable Intelligence on Billion Devices with Deep Learning
(2022)With the proliferation of edge computing and Internet of Things (IoT), billions of edge devices (e.g., smartphone, AR/VR headset, autonomous car, etc) are deployed in our daily life and constantly generating the gigantic ... -
Fluence Map Prediction Using Deep Learning Models - Direct Plan Generation for Pancreas Stereotactic Body Radiation Therapy.
(Frontiers in artificial intelligence, 2020-01)Purpose: Treatment planning for pancreas stereotactic body radiation therapy (SBRT) is a difficult and time-consuming task. In this study, we aim to develop a novel deep learning framework to generate clinical-quality plans ... -
Gaussian Process-Based Models for Clinical Time Series in Healthcare
(2018)Clinical prediction models offer the ability to help physicians make better data-driven decisions that can improve patient outcomes. Given the wealth of data available with the widespread adoption of electronic health records, ... -
Learning deep models via optimal transport distance
(2021)Distribution matching is a core problem in modern deep learning community. Since most tasks are requiring deep models to estimate the true data distribution. For instance, GAN~\cite{goodfellow2014generative} wants to generate ... -
LOW DOSE CT ENHANCEMENT USING DEEP LEARNING METHOD
(2021)Purpose:Deep learning has been widely applied in traditional medical imaging tasks like segmentation and registration. Some fundamental CNN based deep learning methods have shown great potential in low dose CT (LDCT) enhancement. ... -
Machine Learning Approaches to Improve Diagnosis and Management of Mammographic Calcifications
(2021)Currently the most common and effective procedure of early detection of breast cancer is through screening mammography. Mammography detects not only invasive cancers, but also in situ lesions including ductal carcinoma in ... -
Material decomposition from photon-counting CT using a convolutional neural network and energy-integrating CT training labels.
(Physics in medicine and biology, 2022-06-29)<h4>Objective</h4>Photon-counting CT (PCCT) has better dose efficiency and spectral resolution than energy-integrating CT, which is advantageous for material decomposition. Unfortunately, the accuracy of PCCT-based material ...
