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- All Subjects: Medical Imaging
- Creators: Li, Baoxin
- Creators: Li, Jing
Description
Large-scale $\ell_1$-regularized loss minimization problems arise in high-dimensional applications such as compressed sensing and high-dimensional supervised learning, including classification and regression problems. In many applications, it remains challenging to apply the sparse learning model to large-scale problems that have massive data samples with high-dimensional features. One popular and promising strategy is to scaling up the optimization problem in parallel. Parallel solvers run multiple cores on a shared memory system or a distributed environment to speed up the computation, while the practical usage is limited by the huge dimension in the feature space and synchronization problems.
In this dissertation, I carry out the research along the direction with particular focuses on scaling up the optimization of sparse learning for supervised and unsupervised learning problems. For the supervised learning, I firstly propose an asynchronous parallel solver to optimize the large-scale sparse learning model in a multithreading environment. Moreover, I propose a distributed framework to conduct the learning process when the dataset is distributed stored among different machines. Then the proposed model is further extended to the studies of risk genetic factors for Alzheimer's Disease (AD) among different research institutions, integrating a group feature selection framework to rank the top risk SNPs for AD. For the unsupervised learning problem, I propose a highly efficient solver, termed Stochastic Coordinate Coding (SCC), scaling up the optimization of dictionary learning and sparse coding problems. The common issue for the medical imaging research is that the longitudinal features of patients among different time points are beneficial to study together. To further improve the dictionary learning model, I propose a multi-task dictionary learning method, learning the different task simultaneously and utilizing shared and individual dictionary to encode both consistent and changing imaging features.
In this dissertation, I carry out the research along the direction with particular focuses on scaling up the optimization of sparse learning for supervised and unsupervised learning problems. For the supervised learning, I firstly propose an asynchronous parallel solver to optimize the large-scale sparse learning model in a multithreading environment. Moreover, I propose a distributed framework to conduct the learning process when the dataset is distributed stored among different machines. Then the proposed model is further extended to the studies of risk genetic factors for Alzheimer's Disease (AD) among different research institutions, integrating a group feature selection framework to rank the top risk SNPs for AD. For the unsupervised learning problem, I propose a highly efficient solver, termed Stochastic Coordinate Coding (SCC), scaling up the optimization of dictionary learning and sparse coding problems. The common issue for the medical imaging research is that the longitudinal features of patients among different time points are beneficial to study together. To further improve the dictionary learning model, I propose a multi-task dictionary learning method, learning the different task simultaneously and utilizing shared and individual dictionary to encode both consistent and changing imaging features.
ContributorsLi, Qingyang (Author) / Ye, Jieping (Thesis advisor) / Xue, Guoliang (Thesis advisor) / He, Jingrui (Committee member) / Wang, Yalin (Committee member) / Li, Jing (Committee member) / Arizona State University (Publisher)
Created2017
Description
Alzheimer’s Disease (AD), a neurodegenerative disease is a progressive disease that affects the brain gradually with time and worsens. Reliable and early diagnosis of AD and its prodromal stages (i.e. Mild Cognitive Impairment(MCI)) is essential. Fluorodeoxyglucose (FDG) positron emission tomography (PET) measures the decline in the regional cerebral metabolic rate for glucose, offering a reliable metabolic biomarker even on presymptomatic AD patients. PET scans provide functional information that is unique and unavailable using other types of imaging. The computational efficacy of FDG-PET data alone, for the classification of various Alzheimer’s Diagnostic categories (AD, MCI (LMCI, EMCI), Control) has not been studied. This serves as motivation to correctly classify the various diagnostic categories using FDG-PET data. Deep learning has recently been applied to the analysis of structural and functional brain imaging data. This thesis is an introduction to a deep learning based classification technique using neural networks with dimensionality reduction techniques to classify the different stages of AD based on FDG-PET image analysis.
This thesis develops a classification method to investigate the performance of FDG-PET as an effective biomarker for Alzheimer's clinical group classification. This involves dimensionality reduction using Probabilistic Principal Component Analysis on max-pooled data and mean-pooled data, followed by a Multilayer Feed Forward Neural Network which performs binary classification. Max pooled features result into better classification performance compared to results on mean pooled features. Additionally, experiments are done to investigate if the addition of important demographic features such as Functional Activities Questionnaire(FAQ), gene information helps improve performance. Classification results indicate that our designed classifiers achieve competitive results, and better with the additional of demographic features.
This thesis develops a classification method to investigate the performance of FDG-PET as an effective biomarker for Alzheimer's clinical group classification. This involves dimensionality reduction using Probabilistic Principal Component Analysis on max-pooled data and mean-pooled data, followed by a Multilayer Feed Forward Neural Network which performs binary classification. Max pooled features result into better classification performance compared to results on mean pooled features. Additionally, experiments are done to investigate if the addition of important demographic features such as Functional Activities Questionnaire(FAQ), gene information helps improve performance. Classification results indicate that our designed classifiers achieve competitive results, and better with the additional of demographic features.
ContributorsSingh, Shibani (Author) / Wang, Yalin (Thesis advisor) / Li, Baoxin (Committee member) / Liang, Jianming (Committee member) / Arizona State University (Publisher)
Created2017
Description
Increased LV wall thickness is frequently encountered in transthoracicechocardiography (TTE). While accurate and early diagnosis is clinically important,
given the differences in available therapeutic options and prognosis, an extensive workup
is often required for establishing the diagnosis. I propose the first echo-based, automated
deep learning model with a fusion architecture to facilitate the evaluation and diagnosis
of increased left ventricular (LV) wall thickness. Patients with an established diagnosis
for increased LV wall thickness (hypertrophic cardiomyopathy (HCM), cardiac
amyloidosis (CA), and hypertensive heart disease (HTN)/others) between 1/2015 to
11/2019 at Mayo Clinic Arizona were identified. The cohort was divided into
80%/10%/10% for training, validation, and testing sets, respectively. Six baseline TTE
views were used to optimize a pre-trained InceptionResnetV2 model, each model output
was used to train a meta-learner under a fusion architecture. Model performance was
assessed by multiclass area under the receiver operating characteristic curve (AUROC).
A total of 586 patients were used for the final analysis (194 HCM, 201 CA, and 191
HTN/others). The mean age was 55.0 years, and 57.8% were male. Among the individual
view-dependent models, the apical 4 chamber model had the best performance (AUROC:
HCM: 0.94, CA: 0.73, and HTN/other: 0.87). The final fusion model outperformed all the
view-dependent models (AUROC: CA: 0.90, HCM: 0.93, and HTN/other: 0.92). I
successfully established an automatic end-to-end deep learning model framework that
accurately differentiates the major etiologies of increased LV wall thickness, including
HCM and CA from the background of HTN/other diagnoses.
ContributorsLi, James Shuyue (Author) / Patel, Bhavik (Thesis advisor) / Li, Baoxin (Thesis advisor) / Banerjee, Imon (Committee member) / Arizona State University (Publisher)
Created2022
Description
Deep learning is a sub-field of machine learning in which models are developed to imitate the workings of the human brain in processing data and creating patterns for decision making. This dissertation is focused on developing deep learning models for medical imaging analysis of different modalities for different tasks including detection, segmentation and classification. Imaging modalities including digital mammography (DM), magnetic resonance imaging (MRI), positron emission tomography (PET) and computed tomography (CT) are studied in the dissertation for various medical applications. The first phase of the research is to develop a novel shallow-deep convolutional neural network (SD-CNN) model for improved breast cancer diagnosis. This model takes one type of medical image as input and synthesizes different modalities for additional feature sources; both original image and synthetic image are used for feature generation. This proposed architecture is validated in the application of breast cancer diagnosis and proved to be outperforming the competing models. Motivated by the success from the first phase, the second phase focuses on improving medical imaging synthesis performance with advanced deep learning architecture. A new architecture named deep residual inception encoder-decoder network (RIED-Net) is proposed. RIED-Net has the advantages of preserving pixel-level information and cross-modality feature transferring. The applicability of RIED-Net is validated in breast cancer diagnosis and Alzheimer’s disease (AD) staging. Recognizing medical imaging research often has multiples inter-related tasks, namely, detection, segmentation and classification, my third phase of the research is to develop a multi-task deep learning model. Specifically, a feature transfer enabled multi-task deep learning model (FT-MTL-Net) is proposed to transfer high-resolution features from segmentation task to low-resolution feature-based classification task. The application of FT-MTL-Net on breast cancer detection, segmentation and classification using DM images is studied. As a continuing effort on exploring the transfer learning in deep models for medical application, the last phase is to develop a deep learning model for both feature transfer and knowledge from pre-training age prediction task to new domain of Mild cognitive impairment (MCI) to AD conversion prediction task. It is validated in the application of predicting MCI patients’ conversion to AD with 3D MRI images.
ContributorsGao, Fei (Author) / Wu, Teresa (Thesis advisor) / Li, Jing (Committee member) / Yan, Hao (Committee member) / Patel, Bhavika (Committee member) / Arizona State University (Publisher)
Created2019
Description
There is intense interest in adopting computer-aided diagnosis (CAD) systems, particularly those developed based on deep learning algorithms, for applications in a number of medical specialties. However, success of these CAD systems relies heavily on large annotated datasets; otherwise, deep learning often results in algorithms that perform poorly and lack generalizability. Therefore, this dissertation seeks to address this critical problem: How to develop efficient and effective deep learning algorithms for medical applications where large annotated datasets are unavailable. In doing so, we have outlined three specific aims: (1) acquiring necessary annotations efficiently from human experts; (2) utilizing existing annotations effectively from advanced architecture; and (3) extracting generic knowledge directly from unannotated images. Our extensive experiments indicate that, with a small part of the dataset annotated, the developed deep learning methods can match, or even outperform those that require annotating the entire dataset. The last part of this dissertation presents the importance and application of imaging in healthcare, elaborating on how the developed techniques can impact several key facets of the CAD system for detecting pulmonary embolism. Further research is necessary to determine the feasibility of applying these advanced deep learning technologies in clinical practice, particularly when annotation is limited. Progress in this area has the potential to enable deep learning algorithms to generalize to real clinical data and eventually allow CAD systems to be employed in clinical medicine at the point of care.
ContributorsZhou, Zongwei (Author) / Liang, Jianming (Thesis advisor) / Shortliffe, Edward H (Committee member) / Greenes, Robert A (Committee member) / Li, Baoxin (Committee member) / Arizona State University (Publisher)
Created2021