Biomedical Engineering and Bioengineering Commons^™

Challenges Of Constructing Entrainment Map For Arbitrary Circadian Models, Yuxuan (Nelson) Wu

Honors Theses

The entrainment map, developed by Dr.Diekman and Dr.Bose, is claimed to be a 1-dimensional map that produces a better prediction for phase-locking than methods than the phase response curve for circadian models. In his paper, he constructs the entrainment map for the two-dimensional circadian model, the Novak-Tyson model, and the other two higher-dimensional circadian models. For this thesis, we concentrate on exploring if it is viable to construct the entrainment map for other circadian models that are not included in his paper: the Becker-Weimann model and the Relogio model. In addition, we discuss the challenges of constructing the entrainment map …

Universal Design In Bci: Deep Learning Approaches For Adaptive Speech Brain-Computer Interfaces, Srdjan Lesaja

Theses and Dissertations

In the last two decades, there have been many breakthrough advancements in non-invasive and invasive brain-computer interface (BCI) systems. However, the majority of BCI model designs still follow a paradigm whereby neural signals are preprocessed and task-related features extracted using static, and generally customized, data-independent designs. Such BCI designs commonly optimize narrow task performance over generalizability, adaptability, and robustness, which is not well suited to meeting individual user needs. If one day BCIs are to be capable of decoding our higher-order cognitive commands and conceptual maps, their designs will need to be adaptive architectures that will evolve and grow in …

Eye Movement And Pupil Measures: A Review, Bhanuka Mahanama, Yasith Jayawardana, Sundararaman Rengarajan, Gavindya Jayawardena, Leanne Chukoskie, Joseph Snider, Sampath Jayarathna

Computer Science Faculty Publications

Our subjective visual experiences involve complex interaction between our eyes, our brain, and the surrounding world. It gives us the sense of sight, color, stereopsis, distance, pattern recognition, motor coordination, and more. The increasing ubiquity of gaze-aware technology brings with it the ability to track gaze and pupil measures with varying degrees of fidelity. With this in mind, a review that considers the various gaze measures becomes increasingly relevant, especially considering our ability to make sense of these signals given different spatio-temporal sampling capacities. In this paper, we selectively review prior work on eye movements and pupil measures. We first …

Customer Gaze Estimation In Retail Using Deep Learning, Shashimal Senarath, Primesh Pathirana, Dulani Meedeniya, Sampath Jayarathna

Computer Science Faculty Publications

At present, intelligent computing applications are widely used in different domains, including retail stores. The analysis of customer behaviour has become crucial for the benefit of both customers and retailers. In this regard, the concept of remote gaze estimation using deep learning has shown promising results in analyzing customer behaviour in retail due to its scalability, robustness, low cost, and uninterrupted nature. This study presents a three-stage, three-attention-based deep convolutional neural network for remote gaze estimation in retail using image data. In the first stage, we design a mechanism to estimate the 3D gaze of the subject using image data …

3d Printing In Cardiology: A Review Of Applications And Roles For Advanced Cardiac Imaging, Ellen M. Lindquist, Jordan M. Gosnell, Sana K. Khan, John L. Byl, Weihua Zhou, Jingfeng Jiang, Et. Al.

Michigan Tech Publications, Part 1

With the rate of cardiovascular diseases in the U.S increasing throughout the years, there is a need for developing more advanced treatment plans that can be tailored to specific patients and scenarios. The development of 3D printing is rapidly gaining acceptance into clinical cardiology.

In this review, key technologies used in 3D printing are briefly summarized, particularly, the use of artificial intelligence (AI), open-source tools like MeshLab and MeshMixer, and 3D printing techniques such as fused deposition molding (FDM) and polyjet are reviewed. The combination of 3D printing, multiple image integration, and augmented reality may greatly enhance data visualization …

Deep Learning Predicts Ebv Status In Gastric Cancer Based On Spatial Patterns Of Lymphocyte Infiltration, Baoyi Zhang, Kevin Yao, Min Xu, Jia Wu, Chao Cheng

Computer Vision Faculty Publications

EBV infection occurs in around 10% of gastric cancer cases and represents a distinct subtype, characterized by a unique mutation profile, hypermethylation, and overexpression of PD-L1. Moreover, EBV positive gastric cancer tends to have higher immune infiltration and a better prognosis. EBV infection status in gastric cancer is most commonly determined using PCR and in situ hybridization, but such a method requires good nucleic acid preservation. Detection of EBV status with histopathology images may complement PCR and in situ hybridization as a first step of EBV infection assessment. Here, we developed a deep learning-based algorithm to directly predict EBV infection …

Bone Quality And Fractures In Women With Osteoporosis Treated With Bisphosphonates For 1 To 14 Years, Hartmut H. Malluche, Jin Chen, Florence Lima, Lucas J. Liu, Marie-Claude Monier-Faugere, David A. Pienkowski

Internal Medicine Faculty Publications

Oral bisphosphonates are the primary medication for osteoporosis, but concerns exist regarding potential bone-quality changes or low-energy fractures. This cross-sectional study used artificial intelligence methods to analyze relationships among bisphosphonate treatment duration, a wide variety of bone-quality parameters, and low-energy fractures. Fourier transform infrared spectroscopy and histomorphometry quantified bone-quality parameters in 67 osteoporotic women treated with oral bisphosphonates for 1 to 14 years. Artificial intelligence methods established two models relating bisphosphonate treatment duration to bone-quality changes and to low-energy clinical fractures. The model relating bisphosphonate treatment duration to bone quality demonstrated optimal performance when treatment durations of 1 to 8 …

Molecular Dynamics Simulations Of Self-Assemblies In Nature And Nanotechnology, Phu Khanh Tang

Dissertations, Theses, and Capstone Projects

Nature usually divides complex systems into smaller building blocks specializing in a few tasks since one entity cannot achieve everything. Therefore, self-assembly is a robust tool exploited by Nature to build hierarchical systems that accomplish unique functions. The cell membrane distinguishes itself as an example of Nature’s self-assembly, defining and protecting the cell. By mimicking Nature’s designs using synthetically designed self-assemblies, researchers with advanced nanotechnological comprehension can manipulate these synthetic self-assemblies to improve many aspects of modern medicine and materials science. Understanding the competing underlying molecular interactions in self-assembly is always of interest to the academic scientific community and industry. …

Gradient Free Sign Activation Zero One Loss Neural Networks For Adversarially Robust Classification, Yunzhe Xue

Dissertations

The zero-one loss function is less sensitive to outliers than convex surrogate losses such as hinge and cross-entropy. However, as a non-convex function, it has a large number of local minima, andits undifferentiable attribute makes it impossible to use backpropagation, a method widely used in training current state-of-the-art neural networks. When zero-one loss is applied to deep neural networks, the entire training process becomes challenging. On the other hand, a massive non-unique solution probably also brings different decision boundaries when optimizing zero-one loss, making it possible to fight against transferable adversarial examples, which is a common weakness in deep learning …

Towards Adversarial Robustness With 01 Lossmodels, And Novel Convolutional Neural Netsystems For Ultrasound Images, Meiyan Xie

Dissertations

This dissertation investigates adversarial robustness with 01 loss models and a novel convolutional neural net systems for vascular ultrasound images.

In the first part, the dissertation presents stochastic coordinate descent for 01 loss and its sensitivity to adversarial attacks. The study here suggests that 01 loss may be more resilient to adversarial attacks than the hinge loss and further work is required.

In the second part, this dissertation proposes sign activation network with a novel gradient-free stochastic coordinate descent algorithm and its ensembling model. The study here finds that the ensembling model gives a high minimum distortion (as measured by …

Photoacoustic Imaging, Feature Extraction, And Machine Learning Implementation For Ovarian And Colorectal Cancer Diagnosis, Eghbal Amidi

McKelvey School of Engineering Graduate Student Theses & Dissertations

Among all cancers related to women’s reproductive systems, ovarian cancer has the highest mortality rate. Pelvic examination, transvaginal ultrasound (TVUS), and blood testing for cancer antigen 125 (CA-125), are the conventional screening tools for ovarian cancer, but they offer very low specificity. Other tools, such as magnetic resonance imaging (MRI), computed tomography (CT), and positron emission tomography (PET), also have limitations in detecting small lesions. In the USA, considering men and women separately, colorectal cancer is the third most common cause of death related to cancer; for men and women combined, it is the second leading cause of cancer deaths. …

Automated Wound Segmentation And Dimension Measurement Using Rgb-D Image, Chih-Yun Pai

USF Tampa Graduate Theses and Dissertations

Accurate pressure ulcer (PrU) measurement is critical in assessing the effectiveness of PrU treatment. The traditional measurement process is manual, subjective, and requires frequent contact with the wound. The manual measurement relies on human observation which makes the measurement inconsistent, and the frequent contact with the wound increases risk of contamination or infection. The purpose of this research was to develop an automatic Pressure Ulcer Monitoring System (PrUMS) using a depth camera to provide automated, non-contact wound measurement. In this dissertation, 1) a wound segmentation with traditional machine learning method, which combines the color classification using K-Nearest Neighbors and the …

Development Of Deep Learning Neural Network For Ecological And Medical Images, Shaobo Liu

Dissertations

Deep learning in computer vision and image processing has attracted attentions from various fields including ecology and medical image. Ecologists are interested in finding an effective model structure to classify different species. Tradition deep learning model use a convolutional neural network, such as LeNet, AlexNet, VGG models, residual neural network, and inception models, are first used on classifying bee wing and butterfly datasets. However, insufficient data sample and unbalanced samples in each class have caused a poor accuracy. To make improvement the test accuracy, data augmentation and transfer learning are applied. Recently developed deep learning framework based on mathematical morphology …

Assessment And Diagnosis Of Human Colorectal And Ovarian Cancer Using Optical Imaging And Computer-Aided Diagnosis, Yifeng Zeng

McKelvey School of Engineering Graduate Student Theses & Dissertations

Tissue optical scattering has recently emerged as an important diagnosis parameter associated with early tumor development and progression. To characterize the differences between benign and malignant colorectal tissues, we have created an automated optical scattering coefficient mapping algorithm using an optical coherence tomography (OCT) system. A novel feature called the angular spectrum index quantifies the scattering coefficient distribution. In addition to scattering, subsurface morphological changes are also associated with the development of colorectal cancer. We have observed a specific mucosa structure indicating normal human colorectal tissue, and have developed a real-time pattern recognition neural network to localize this specific structure …

Using Deep Learning To Analyze Materials In Medical Images, Carson Molder

Computer Science and Computer Engineering Undergraduate Honors Theses

Modern deep learning architectures have become increasingly popular in medicine, especially for analyzing medical images. In some medical applications, deep learning image analysis models have been more accurate at predicting medical conditions than experts. Deep learning has also been effective for material analysis on photographs. We aim to leverage deep learning to perform material analysis on medical images. Because material datasets for medicine are scarce, we first introduce a texture dataset generation algorithm that automatically samples desired textures from annotated or unannotated medical images. Second, we use a novel Siamese neural network called D-CNN to predict patch similarity and build …

An Automated Framework For Connected Speech Evaluation Of Neurodegenerative Disease: A Case Study In Parkinson's Disease, Sai Bharadwaj Appakaya

USF Tampa Graduate Theses and Dissertations

Neurodegenerative diseases affect millions of people around the world. The progressive degeneration worsens the symptoms, heavily impacting the quality of life of the patients as well as the caregivers. Speech production is one of the physiological processes affected by neurodegenerative diseases like Alzheimer’s disease, amyotrophic lateral sclerosis (ALS) and Parkinson’s disease (PD). Speech is the most basic form of communication, and the effect of neurodegeneration degrades speech production, thereby reducing social interaction and mental well-being. PD is the second most common neurodegenerative disease affecting speech production in 90% of the diagnosed individuals. Speech analysis methods for PD in clinical methods …

Predictions Of Knee Joint Contact Forces Using Only Kinematic Inputs With A Recurrent Neural Network, Kaileigh Elisabeth Estler

Human Movement Studies & Special Education Theses & Dissertations

BACKGROUND: Knee joint contact (bone on bone) forces are commonly estimated using surrogate measures such as external knee adduction moments (with limited success) or musculoskeletal modeling (more successful). Despite its capabilities, modeling is not optimal for clinicians or persons with limited experience and knowledge. Therefore, the purpose of this study was to design a novel prediction method for knee joint contact forces that is equal or more accurate than modeling, yet simplistic in terms of required inputs. METHODS: This study included all six subjects’ (71.3±6.5kg, 1.7±0.1m) data from the opensource “Grand Challenge” datasets (simtk.org) and two subjects from the "CAMS" …

Analysis Of Inflammation, Metabolic And Clinical Markers In Predicting Fat Mass In Hiv-Positive Males Using Artificial Neural Network, Nurul Farhah Shamsuddin

Student Works (2020-2029)

Increased inflammation has been discovered in people living with Human Immunodeficiency Virus (HIV), which has been associated with the development and advancement of metabolic diseases. Immune activation and inflammation have been revealed to affect the redistribution of fat in people living with HIV (PLWH) whether the individual is on treatment with antiretroviral therapy (ART) or treatment naïve. Therefore, the assessment of body fat composition of PLWH based on markers of inflammation is important to develop keen diagnosis and prognosis of medical conditions facing by these patients, for example lipodystrophy syndrome, metabolic syndrome, dyslipidemia and diabetes mellitus. The purpose of this …

An Improved Clipped Sub-Histogram Equalization Technique Using Optimized Local Contrast Factor For Mammogram Image Analysis, Nurshafira Hazim Chan

Student Works (2020-2029)

Mammography has been known worldwide as the most common imaging modalities utilized for early detection of breast cancer. The mammographic images produced are in greyscale, however they often produced poor contrast images, non-uniform illumination, and the image often contain artefacts and noise. These limitations can be overcame during the pre-processing stage by improving the image enhancement process. Therefore, in this research an optimized enhancement framework is developed where the local contrast factor is manipulated to preserve details of the image. This technique aims to improve the overall image visibility without altering histogram of the original image, which will affect the …

Deep Learning-Based Breast Cancer Detection And Classification Using Histopathology Images, Ghulam Murtaza

Student Works (2020-2029)

Cancer disease is drastically increasing worldwide over the past few years. Among all types of cancers in women, breast cancer (BrC) is the main cause of abnormal deaths. For a confident diagnosis of BrC, histopathology (Hp) images are usually suggested by the doctors. BrC detection is a diagnostic test for benign (non-cancerous) and malignant (cancerous) breast tumors (BrT). Once the BrT is diagnosed, then it needs to be classified for subtypes of benign and malignant to start specific treatment. Several studies developed BrC detection and classification models using Hp images. However, the existing models required high computational resources, long training …

Deep Learning For Task-Based Image Quality Assessment In Medical Imaging, Weimin Zhou

McKelvey School of Engineering Graduate Student Theses & Dissertations

It has been advocated to use objective measures of image quality (IQ) for assessing and optimizing medical imaging systems. Objective measures of IQ quantify the performance of an observer at a specific diagnostic task. Binary signal detection tasks and joint signal detection and localization (detection-localization) tasks are commonly considered in medical imaging. When optimizing imaging systems for binary signal detection tasks, the performance of the Bayesian Ideal Observer (IO) has been advocated for use as a figure-of-merit (FOM). The IO maximizes the observer performance that is summarized by the receiver operating characteristic (ROC) curve. When signal detection-localization tasks are considered, …

Three-Dimensional (3d) Reconstruction Of Computer Tomography Cardiac Images Using Visualization Toolkit (Vtk), Mohammad Zahid Zamaludin

Student Works (2020-2029)

Cardiac study proves to be important to diagnose and treat cardiovascular disease that accounts for 20% of death in the world. Three-dimensional (3D) images of the heart are important to understand its real structure, functions and physical abnormalities occurring in the tissue. The objective of this paper is to reconstruct 3D images from 2D CT Scan slices of the cardiac region of interest using Visualisation Toolkit (VTK). The Visualization Toolkit (VTK) is non-propriety software with state-of-the-art tools for 3D rendering that is easily customizable programming. 3D images are reconstructed using surface rendering technique and volume rendering technique. Volume rendering shows …

A Hybrid Gene Selection Strategy Based On Fisher And Ant Colony Optimization Algorithm For Breast Cancer Classification, Mohammed Hamim, Ismail El Moudden, Mohan D. Pant, Hicham Moutachaouik, Mustapha Hain

EVMS School of Health Professions Faculty Publications

Breast cancer poses the greatest threat to human life and especially to women's life. Despite the progress made in data mining technology in recent years, the ability to predict and diagnose such fatal diseases based on gene expression data still reveals a limited prediction performance, which may not be surprising since most of the genes in expression data are believed to be irrelevant or redundant. The dimensionality reduction process may be considered as a crucial step to analyze gene expression data, as it can reduce the high dimensionality of the breast cancer datasets, which may result into a better prediction …

Perceptually Improved Medical Image Translations Using Conditional Generative Adversarial Networks, Anurag Vaidya

Honors Theses

Magnetic resonance imaging (MRI) can help visualize various brain regions. Typical MRI sequences consist of T1-weighted sequence (favorable for observing large brain structures), T2-weighted sequence (useful for pathology), and T2-FLAIR scan (useful for pathology with suppression of signal from water). While these different scans provide complementary information, acquiring them leads to acquisition times of ~1 hour and an average cost of $2,600, presenting significant barriers. To reduce these costs associated with brain MRIs, we present pTransGAN, a generative adversarial network capable of translating both healthy and unhealthy T1 scans into T2 scans. We show that the addition of non-adversarial …

Applied Machine Learning In Extrusion-Based Bioprinting, Shuyu Tian

Theses and Dissertations

Optimization of extrusion-based bioprinting (EBB) parameters have been systematically conducted through experimentation. However, the process is time and resource-intensive and not easily translatable across different laboratories. A machine learning (ML) approach to EBB parameter optimization can accelerate this process for laboratories across the field through training using data collected from published literature. In this work, regression-based and classification-based ML models were investigated for their abilities to predict printing outcomes of cell viability and filament diameter for cell-containing alginate and gelatin composite hydrogels. Regression-based models were investigated for their ability to predict suitable extrusion pressure given desired cell viability when keeping …

Biocybersecurity: A Converging Threat As An Auxiliary To War, Lucas Potter, Orlando Ayala, Xavier-Lewis Palmer

Engineering Technology Faculty Publications

Biodefense is the discipline of ensuring biosecurity with respect to select groups of organisms and limiting their spread. This field has increasingly been challenged by novel threats from nature that have been weaponized such as SARS, Anthrax, and similar pathogens, but has emerged victorious through collaboration of national and world health groups. However, it may come under additional stress in the 21st century as the field intersects with the cyberworld-- a world where governments have already been struggling to keep up with cyber attacks from small to state-level actors as cyberthreats have been relied on to level the playing field …

Fmri Feature Extraction Model For Adhd Classification Using Convolutional Neural Network, Senuri De Silva, Sanuwani Udara Dayarathna, Gangani Ariyarathne, Dulani Meedeniya, Sampath Jayarathna

Computer Science Faculty Publications

Biomedical intelligence provides a predictive mechanism for the automatic diagnosis of diseases and disorders. With the advancements of computational biology, neuroimaging techniques have been used extensively in clinical data analysis. Attention deficit hyperactivity disorder (ADHD) is a psychiatric disorder, with the symptomology of inattention, impulsivity, and hyperactivity, in which early diagnosis is crucial to prevent unwelcome outcomes. This study addresses ADHD identification using functional magnetic resonance imaging (fMRI) data for the resting state brain by evaluating multiple feature extraction methods. The features of seed-based correlation (SBC), fractional amplitude of low-frequency fluctuation (fALFF), and regional homogeneity (ReHo) are comparatively applied to …

Combining Cryo-Em Density Map And Residue Contact For Protein Secondary Structure Topologies, Maytha Alshammari, Jing He

Computer Science Faculty Publications

Although atomic structures have been determined directly from cryo-EM density maps with high resolutions, current structure determination methods for medium resolution (5 to 10 Å) cryo-EM maps are limited by the availability of structure templates. Secondary structure traces are lines detected from a cryo-EM density map for α-helices and β-strands of a protein. A topology of secondary structures defines the mapping between a set of sequence segments and a set of traces of secondary structures in three-dimensional space. In order to enhance accuracy in ranking secondary structure topologies, we explored a method that combines three sources of information: a set …

Identification Of Ecg Anomalies Through Deep Deterministic Learning, Uzair Iqbal

Student Works (2020-2029)

Electrocardiography (ECG) is a primary diagnostic tool for measuring the malfunctioning of the heart muscles in the context of morbidity of different cardiac diseases and arrhythmia. Different existing techniques and methods delivered accurate cardiac diseases myocardial infarction (heart stroke) and atrial fibrillation recognition. However, there are still some flaws in existing methods like recognition of special myocardial infarction situation flattened T wave in “Non-Specific ST-T Changes (nsst-t)” and reduction of computational cost in cardiac diseases recognition. Accurate recognition of cardiac diseases along with least computational complexity and feature analysis of flattened T wave in myocardial infarction remains an open job. …

Analysis Of Glutathione Supplementation Effects On Female Metabolic Syndrome Condition Using Classification Techniques, Nur Rasyidah Hasan Basri

Student Works (2020-2029)

Lesser known to the public that main antioxidants in our body, Glutathione are also said to influence the metabolic syndrome (MS) condition. Oral supplementation consisting of glutathione precursors and vitamin C introduced in the study was to improve glutathione (GSH) status of the consumer and the main objective of this study is to investigate GSH effects on selected MS condition. Several known studies had proven that oral supplementation improved GSH level of consumer. However, there is no definite proof on how it’s affecting MS parameters. Also, there is less study carried out on the relationship between glutathione levels and metabolic …