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Volumetric medical image segmentation with deep learning pipelines
(University of Missouri--Columbia, 2020)
Automated semantic segmentation in the domain of medical imaging can enable a faster, more reliable, and more affordable clinical workflow. Fully convolutional networks (FCNs) have been heavily used in this area due to the ...
AMD, analysis of mood dysregulation : a machine learning approach
(University of Missouri--Columbia, 2016)
There is a popular saying, "Stress kills." This statement can be true with repeated exposures to psychological mood dysregulation, which can lead to or worsen stress related conditions such as heart disease and cancer. ...
Augmenting biological pathway extraction with synthetic data and active learning
(University of Missouri--Columbia, 2022)
The corpus of biomedical literature is growing rapidly as many papers are recorded in PubMed every day. These papers often contain high-quality biological pathways in their figures/text, which are great resources for ...
Deep learning methods for 360 monocular depth estimation and point cloud semantic segmentation
(University of Missouri--Columbia, 2022)
Monocular depth estimation and point cloud segmentation are essential tasks for 3D scene understanding in computer vision. Depth estimation for omnidirectional images is challenging due to the spherical distortion issue and the availability of large...
Advance : adversarial collaborative learning for detection and verification of artificially created examples
(University of Missouri--Columbia, 2023)
Adversarial learning methods have gained significant popularity in generating deceptive yet convincingly authentic data. While these techniques have proven beneficial for advancing artificial intelligence, they also give ...
Deep learning-based solutions for electron microscopy image analysis
(University of Missouri--Columbia, 2023)
Electron microscopy (EM) enables capturing high resolution images of very small structures in biological and non-biological specimens such as membrane proteins, viruses, subcellular structures, nanoparticles, or material ...
Three dimensional reconstruction of plant roots via low energy x-ray computed tomography
(University of Missouri--Columbia, 2018)
Plant roots are vital organs for water and nutrient uptake. The structure and spatial distribution of plant roots in the soil affects a plant's physiological functions such as soil-based resource acquisition, yield and its ...
Intelligent user interfaces for internet-of-things based web applications
(University of Missouri--Columbia, 2016)
[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT REQUEST OF AUTHOR.] There is a growing need for Intelligent User Interfaces to visually make sense of the enormous data that is being created within web applications that ...
Single and multi-object video tracking using local and deep architectures
(University of Missouri--Columbia, 2022)
Moving object tracking is a fundamental computer vision task with a wide variety of real-life applications ranging from surveillance and autonomous systems to biomedical video analysis. A robust, accurate, scalable, and ...
Fuzzy-based conversational recommender for data-intensive science gateway applications
(University of Missouri--Columbia, 2018)
[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] Neuro-scientists are increasingly relying on parallel and distributed computing resources for analysis and visualization of their neuron simulations. ...
Fuzzy-based conversational recommender for data-intensive science gateway applications
(University of Missouri--Columbia, 2018)
Neuro-scientists are increasingly relying on parallel and distributed computing resources for analysis and visualization of their neuron simulations. Although science gateways (SG) have democratized relevant high ...
Out-of-core image techniques with extensions for WAMI
(University of Missouri--Columbia, 2019)
[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT REQUEST OF AUTHOR.] Out-of-core image techniques provide for interactive visualization, analysis, and processing of extremely large images exceeding primary memory. This ...
Tensor completion methods with provable consistency and fairness guarantees for recommender systems
(University of Missouri--Columbia, 2023)
[EMBARGOED UNTIL 8/1/2024] This thesis introduces novel consistency-based approaches for solving nonnegative/ positive matrix and tensor completion problems in the context of recommender systems. The proposed framework ...
Custom templates based heterogeneous resource allocation for data-intensive applications
(University of Missouri--Columbia, 2020)
[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI-COLUMBIA AT REQUEST OF AUTHOR.] The increase of data-intensive applications in science and engineering fields (i.e., bioinformatics, cybermanufacturing) demand the use of ...
Multi-modal and multi-dimensional biomedical image data analysis using deep learning
(University of Missouri--Columbia, 2023)
There is a growing need for the development of computational methods and tools for automated, objective, and quantitative analysis of biomedical signal and image data to facilitate disease and treatment monitoring, early ...
Application of deep reinforcement learning for battery design
(University of Missouri--Columbia, 2020)
[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT REQUEST OF AUTHOR.] The conventional material research and development are mainly driven by human intuition, labor, and manual decision. It is ineffective and inefficient. ...
Deep heterogeneous superpixel neural networks for image analysis and feature extraction
(University of Missouri--Columbia, 2021)
Lately, deep convolutional neural networks are rapidly transforming and enhancing computer vision accuracy and performance, and pursuing higher-level and interpretable object recognition. Superpixel-based methodologies ...
Image matching and image super-resolution via deep learning
(University of Missouri--Columbia, 2018)
The advancement of 3D depth sensors, such as LIDAR (Light Detection And Ranging) scanners, has provided an effective alternative to traditional CAD-based and image-based approaches for 3D modeling. The output of the 3D ...
Deep learning and DCT-based hand-crafted features for computer vision tasks
(University of Missouri--Columbia, 2022)
Feature extraction and matching are critical components for many computer vision tasks including camera pose estimation, 3D reconstruction, simultaneous localization and mapping (SLAM), and object tracking, etc. Features ...
KOLAM : human computer interfaces fro visual analytics in big data imagery
(University of Missouri--Columbia, 2018)
In the present day, we are faced with a deluge of disparate and dynamic information from multiple heterogeneous sources. Among these are the big data imagery datasets that are rapidly being generated via mature acquisition ...