Open Access. Powered by Scholars. Published by Universities.®
Other Biomedical Engineering and Bioengineering Commons™
Open Access. Powered by Scholars. Published by Universities.®
- Discipline
-
- Computer Engineering (2)
- Other Computer Engineering (2)
- Artificial Intelligence and Robotics (1)
- Bioinformatics (1)
- Biomaterials (1)
-
- Biomedical (1)
- Biomedical Devices and Instrumentation (1)
- Chemicals and Drugs (1)
- Complex Mixtures (1)
- Computer Sciences (1)
- Computer and Systems Architecture (1)
- Data Science (1)
- Electrical and Computer Engineering (1)
- Life Sciences (1)
- Medicine and Health Sciences (1)
- Nanomedicine (1)
- Nanotechnology (1)
- Pharmaceutical Preparations (1)
- Physical Sciences and Mathematics (1)
- Systems and Integrative Engineering (1)
- Institution
- Publication
- Publication Type
Articles 1 - 5 of 5
Full-Text Articles in Other Biomedical Engineering and Bioengineering
Fourier-Feature Mlp Toolkit For Gpu-Accelerated Cardiac-Mri 4dcmr Strain Analysis, Aarnav T. Sabale, Marco A. Prado, Craig J. Goergen
Fourier-Feature Mlp Toolkit For Gpu-Accelerated Cardiac-Mri 4dcmr Strain Analysis, Aarnav T. Sabale, Marco A. Prado, Craig J. Goergen
Discovery Undergraduate Interdisciplinary Research Internship
This paper explores the embedding of a Fourier-Feature—enhanced multiplayer perceptron(MLP-FEE) at the heart of a newly refactored python workflow for four-dimensional cardiac-MRI strain quantification demonstrating how a single, compact network can outperform traditional convolution and spline-based methods. The original code, capable of orientation normalization, displacement tracking, and finite-difference strain computation, has been translated and consolidated into pytorch. By injecting sinusoidal positional encodings at the network’s input layer supplied a rich set of high-frequency basis functions hence enabling multilayer MLP to resolve gradients that cubic splines and conventional CNNs typically blur or struggle with. Profiling on an Apple-silicon GPU shows interactive …
Machine Learning And Deep Learning Approaches For Gene Regulatory Network Inference In Plant Species, Sai Teja Mummadi
Machine Learning And Deep Learning Approaches For Gene Regulatory Network Inference In Plant Species, Sai Teja Mummadi
Dissertations, Master's Theses and Master's Reports
The construction of gene regulatory networks (GRNs) is vital for understanding the regulation of metabolic pathways, biological processes, and complex traits during plant growth and responses to environmental cues and stresses. The increasing availability of public databases has facilitated the development of numerous methods for inferring gene regulatory relationships between transcription factors and their targets. However, there is limited research on supervised learning techniques that utilize available regulatory relationships of plant species in public databases.
This study investigates the potential of machine learning (ML), deep learning (DL), and hybrid approaches for constructing GRNs in plant species, specifically Arabidopsis thaliana, …
Breast Density Classification Using Deep Learning, Conrad Thomas Testagrose
Breast Density Classification Using Deep Learning, Conrad Thomas Testagrose
UNF Graduate Theses and Dissertations
Breast density screenings are an accepted means to determine a patient's predisposed risk of breast cancer development. Although the direct correlation is not fully understood, breast cancer risk increases with higher levels of mammographic breast density. Radiologists visually assess a patient's breast density using mammogram images and assign a density score based on four breast density categories outlined by the Breast Imaging and Reporting Data Systems (BI-RADS). There have been efforts to develop automated tools that assist radiologists with increasing workloads and to help reduce the intra- and inter-rater variability between radiologists. In this thesis, I explored two deep-learning-based approaches …
Classifying Electrocardiogram With Machine Learning Techniques, Hillal Jarrar
Classifying Electrocardiogram With Machine Learning Techniques, Hillal Jarrar
Master's Theses
Classifying the electrocardiogram is of clinical importance because classification can be used to diagnose patients with cardiac arrhythmias. Many industries utilize machine learning techniques that consist of feature extraction methods followed by Naive- Bayesian classification in order to detect faults within machinery. Machine learning techniques that analyze vibrational machine data in a mechanical application may be used to analyze electrical data in a physiological application. Three of the most common feature extraction methods used to prepare machine vibration data for Naive-Bayesian classification are the Fourier transform, the Hilbert transform, and the Wavelet Packet transform. Each machine learning technique consists of …
Machine Learning Applied To Colloidal Properties Of Perfluorocarbon Nanoemulsions For Imaging In Ards/Ali, Marco Hosfeld
Machine Learning Applied To Colloidal Properties Of Perfluorocarbon Nanoemulsions For Imaging In Ards/Ali, Marco Hosfeld
Electronic Theses and Dissertations
Acute Respiratory distress Syndrome (ARDS) and Acute Lung Injury (ALI) are inflammatory lung pathologies consisting of non-hydrostatic pulmonary edema leading to hypoxia and impaired gas exchange in the lungs. ARDS/ALI is both difficult to study and treat as it is not in itself a specific pathology but rather a syndrome consisting of many pathologies that vary case by case. It is, however, consistently characterized by an explosive acute inflammatory response in the lung parenchyma leading to hypoxia. Although time has seen to an increase in the understanding of ARDS/ALI, the mortality rate remains in the range of 30-50%. For these …