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Bioimaging and Biomedical Optics Commons™
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- Artificial intelligence (2)
- Artificial image object (1)
- Ball-and-Stick model (1)
- Ball-and-stick model (1)
- Bayesian (1)
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- Brain imaging (1)
- Breast cancer biomarker classification (1)
- Breast--Cancer--Magnetic resonance imaging; Breast--Radiography; Cancer--Diagnosis; Diagnostic imaging; Magnetic resonance imaging; Spectroscopic imaging (1)
- CNN model (1)
- Classification--Computer programs (1)
- Convolutional neural network (1)
- Convolutional neural networks (1)
- DMRI, crossing fibers (1)
- Diffusion MRI (1)
- Disease outcomes (1)
- Glioblastoma (1)
- Image analysis--Data processing (1)
- Image classification (1)
- Least squares--Computer programs (1)
- Machine learning algorithm (1)
- Magnetic resonance imaging (1)
- Multi-omics data (1)
- Nerve (1)
- Neural tracts (1)
- PathCNN (1)
- Pathway image (1)
- Predictive modeling (1)
- RNA sequencing (1)
- Simplified model (1)
- Tractography (1)
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Articles 1 - 6 of 6
Full-Text Articles in Bioimaging and Biomedical Optics
Artificial Image Objects For Classification Of Breast Cancer Biomarkers With Transcriptome Sequencing Data And Convolutional Neural Network Algorithms, Xiangning Chen, Daniel G. Chen, Zhongming Zhao, Justin M. Balko, Jingchun Chen
Artificial Image Objects For Classification Of Breast Cancer Biomarkers With Transcriptome Sequencing Data And Convolutional Neural Network Algorithms, Xiangning Chen, Daniel G. Chen, Zhongming Zhao, Justin M. Balko, Jingchun Chen
School of Medicine Faculty Publications
Background: Transcriptome sequencing has been broadly available in clinical studies. However, it remains a challenge to utilize these data effectively for clinical applications due to the high dimension of the data and the highly correlated expression between individual genes. Methods: We proposed a method to transform RNA sequencing data into artificial image objects (AIOs) and applied convolutional neural network (CNN) algorithms to classify these AIOs. With the AIO technique, we considered each gene as a pixel in an image and its expression level as pixel intensity. Using the GSE96058 (n = 2976), GSE81538 (n = 405), and GSE163882 (n = …
Pathcnn: Interpretable Convolutional Neural Networks For Survival Prediction And Pathway Analysis Applied To Glioblastoma, Jung Hun Oh, Wookjin Choi, Euiseong Ko, Mingon Kang, Allen Tannenbaum, Joseph O. Deasy
Pathcnn: Interpretable Convolutional Neural Networks For Survival Prediction And Pathway Analysis Applied To Glioblastoma, Jung Hun Oh, Wookjin Choi, Euiseong Ko, Mingon Kang, Allen Tannenbaum, Joseph O. Deasy
Computer Science Faculty Research
Motivation: Convolutional neural networks (CNNs) have achieved great success in the areas of image processing and computer vision, handling grid-structured inputs and efficiently capturing local dependencies through multiple levels of abstraction. However, a lack of interpretability remains a key barrier to the adoption of deep neural networks, particularly in predictive modeling of disease outcomes. Moreover, because biological array data are generally represented in a non-grid structured format, CNNs cannot be applied directly. Results: To address these issues, we propose a novel method, called PathCNN, that constructs an interpretable CNN model on integrated multi-omics data using a newly defined pathway image. …
Resolving Intravoxel White Matter Structures In The Human Brain Using Regularized Regression And Clustering, Andrea Hart, Brianna Smith, Sean Smith, Elijah Sales, Jacqueline Hernandez-Camargo, Yarlin Mayor Garcia, Felix Zhan, Lori Griswold, Brian Dunkelberger, Michael R. Schwob, Sharang Chaudhry, Justin Zhan, Laxmi Gewali, Paul Oh
Resolving Intravoxel White Matter Structures In The Human Brain Using Regularized Regression And Clustering, Andrea Hart, Brianna Smith, Sean Smith, Elijah Sales, Jacqueline Hernandez-Camargo, Yarlin Mayor Garcia, Felix Zhan, Lori Griswold, Brian Dunkelberger, Michael R. Schwob, Sharang Chaudhry, Justin Zhan, Laxmi Gewali, Paul Oh
Computer Science Faculty Research
The human brain is a complex system of neural tissue that varies significantly between individuals. Although the technology that delineates these neural pathways does not currently exist, medical imaging modalities, such as diffusion magnetic resonance imaging (dMRI), can be leveraged for mathematical identification. The purpose of this work is to develop a novel method employing machine learning techniques to determine intravoxel nerve number and direction from dMRI data. The method was tested on multiple synthetic datasets and showed promising estimation accuracy and robustness for multi-nerve systems under a variety of conditions, including highly noisy data and imprecision in parameter assumptions.
A Simplified Crossing Fiber Model In Diffusion Weighted Imaging, Sheng Yang, Kaushik Ghosh, Ken Sakaie, Satya S. Sahoo, Sarah J. Ann Carr, Curtis Tatsuoka
A Simplified Crossing Fiber Model In Diffusion Weighted Imaging, Sheng Yang, Kaushik Ghosh, Ken Sakaie, Satya S. Sahoo, Sarah J. Ann Carr, Curtis Tatsuoka
Mathematical Sciences Faculty Research
Diffusion MRI (dMRI) is a vital source of imaging data for identifying anatomical connections in the living human brain that form the substrate for information transfer between brain regions. dMRI can thus play a central role toward our understanding of brain function. The quantitative modeling and analysis of dMRI data deduces the features of neural fibers at the voxel level, such as direction and density. The modeling methods that have been developed range from deterministic to probabilistic approaches. Currently, the Ball-and-Stick model serves as a widely implemented probabilistic approach in the tractography toolbox of the popular FSL software package and …
Investigation Of Magnetic Resonance Imaging And Spectroscopy For The Detection Of Breast Cancer, Robert Thomas Etnire
Investigation Of Magnetic Resonance Imaging And Spectroscopy For The Detection Of Breast Cancer, Robert Thomas Etnire
UNLV Theses, Dissertations, Professional Papers, and Capstones
Magnetic resonance imaging (MRI) of the breast offers an alternative to screening mammography which may benefit those women at high risk for breast cancer, women under the age of 40, and those with dense breast tissue. One concern with MRI is the number of high false positives. Coupling MRI with magnetic resonance spectroscopy(MRS) may lower the number of false positives, and thus improve the diagnostic capabilities of MRI for the clinician. MRS for breast imaging focuses on the total choline containing compounds in the spectra in the suspected breast lesion to analyze areas of concern. The results of the study …
Least Squares Support Vector Machine Based Classification Of Abnormalities In Brain Mr Images, S. Thamarai Selvi, D. Selvathi, R. Ramkumar, Henry Selvaraj
Least Squares Support Vector Machine Based Classification Of Abnormalities In Brain Mr Images, S. Thamarai Selvi, D. Selvathi, R. Ramkumar, Henry Selvaraj
Electrical & Computer Engineering Faculty Research
The manual interpretation of MRI slices based on visual examination by radiologist/physician may lead to missing diagnosis when a large number of MRIs are analyzed. To avoid the human error, an automated intelligent classification system is proposed. This research paper proposes an intelligent classification technique to the problem of classifying four types of brain abnormalities viz. Metastases, Meningiomas, Gliomas, and Astrocytomas. The abnormalities are classified based on Two/Three/ Four class classification using statistical and textural features. In this work, classification techniques based on Least Squares Support Vector Machine (LS-SVM) using textural features computed from the MR images of patient are …