Browsing by Subject "machine learning"
Now showing items 21-40 of 44
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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 ... -
Machine Learning and Precision Medicine in Emergency Medicine: The Basics.
(Cureus, 2021-09)As machine learning (ML) and precision medicine become more readily available and used in practice, emergency physicians must understand the potential advantages and limitations of the technology. This narrative review focuses ... -
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 ... -
Machine Learning with Dirichlet and Beta Process Priors: Theory and Applications
(2010)Bayesian nonparametric methods are useful for modeling data without having to define the complexity of the entire model <italic>a priori</italic>, but rather allowing for this complexity to be determined by the data. Two ... -
Machine Learning-based Techniques to Address Spectral Distortions in Photon Counting X-ray Computed Tomography
(2016)Spectral CT using a photon counting x-ray detector (PCXD) shows great potential for measuring material composition based on energy dependent x-ray attenuation. Spectral CT is especially suited for imaging with K-edge contrast ... -
Modeling and Methodological Advances in Causal Inference
(2021)This thesis presents several novel modeling or methodological advancements to causal inference. First, we investigate the use of propensity score weighting in the randomized trials for covariate adjustment. We introduce ... -
Modeling Biological Systems from Heterogeneous Data
(2008-04-24)The past decades have seen rapid development of numerous high-throughput technologies to observe biomolecular phenomena. High-throughput biological data are inherently heterogeneous, providing information at the various ... -
New Directions in Bandit Learning: Singularities and Random Walk Feedback
(2021)My thesis focuses new directions in bandit learning problems. In Chapter 1, I give an overview of the bandit learning literature, which lays the discussion framework for studies in Chapters 2 and 3. In Chapter 2, I study ... -
Overcoming barriers to the adoption and implementation of predictive modeling and machine learning in clinical care: what can we learn from US academic medical centers?
(JAMIA open, 2020-07)There is little known about how academic medical centers (AMCs) in the US develop, implement, and maintain predictive modeling and machine learning (PM and ML) models. We conducted semi-structured interviews with leaders ... -
Peritumoral CT Radiomic Modelling for Non-local Treatment Failure of Early Stage Non-Small Cell Lung Cancers
(2020)Background: Quantitative medical imaging has been increasingly utilized in modern medicine. The field of radiomics is an emerging subset of quantitative medical imaging. Radiomics can identify a large number of quantitative ... -
Predicting 3-D Deformation Field Maps (DFM) based on Volumetric Cine MRI (VC-MRI) and Artificial Neural Networks for On-board 4D Target Tracking
(2019)Organ and tumor positions are constantly subject to change due to involuntary movement from the gastrointestinal and respiratory systems. In radiation therapy, accurate and precise anatomical localization is critical for ... -
Predicting the risk of rupture for vertebral aneurysm based on geometric features of blood vessels.
(Royal Society open science, 2021-08-11)A significant proportion of the adult population worldwide suffers from cerebral aneurysms. If left untreated, aneurysms may rupture and lead to fatal massive internal bleeding. On the other hand, treatment of aneurysms ... -
Predictive modeling of TBI outcomes in Rwanda: Generalizability of Tanzania developed prognostic models
(2020)Background: Globally, many low-income settings lack diagnostic tools to handle prognosis of TBI patients. In such settings, development of generalizable predictive models which indicate likelihood of patient outcomes may ... -
Programming DNA for molecular-scale temporal barcoding and enzymatic computation
(2020)DNA, the blueprint of life, is more than a carrier of genetic information. It offers a highly programmable substrate that can be used for computing, nanorobotics, and advanced imaging techniques. In this work, we use the ... -
Radiogenomics for Radiation Treatment Assessment of Advanced Lung Cancers
(2019)Background: Radiomics describes the study of converting medical images into high-dimensional quantitative features and following analysis for further decision making and genomics focuses on the understanding genomes of individual ... -
Real-Time and Data-Driven Operation Optimization and Knowledge Discovery for an Enterprise Information System
(2014)An enterprise information system (EIS) is an integrated data-applications platform characterized by diverse, heterogeneous, and distributed data sources. For many enterprises, a number of business processes still depend ... -
Seabird trophic position across three ocean regions tracks ecosystem differences
(Frontiers in Marine Science, 2018-09-07)We analyze recently collected feather tissues from two species of seabirds, the sooty tern (Onychoprion fuscatus) and brown noddy (Anous stolidus), in three ocean regions (North Atlantic, North Pacific, and South Pacific) ... -
Semantic Understanding for Augmented Reality and Its Applications
(2020-04-08)Although augmented reality (AR) devices and developer toolkits are becoming increasingly ubiquitous, current AR devices lack a semantic understanding of the user’s environment. Semantic understanding in an AR context is ... -
Statistical Modeling to Improve Buried Target Detection with a Forward-Looking Ground-Penetrating Radar
(2017)Forward-looking ground-penetrating radar (FLGPR) has recently been investigated as a remote sensing modality for buried target detection (e.g., landmines and improvised explosive devices (IEDs) ). In this context, raw FLGPR ... -
Testing for Unobserved Heterogeneity via K-Means Clustering
(2019-07-15)
