Donald Brown

Donald Brown
University of Virginia | UVa · School of Data Science

Doctor of Philosophy

About

318
Publications
186,138
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5,676
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Introduction
My work has focused on mathematical modeling and machine learning techniques that enable the combination of different types of data for prediction, diagnosis, estimation, and process design. At the center of this work is the development and use of deep learning neural network architectures combined with Bayesian inferential methods for point processes in high dimensional feature spaces. This work has direct applicability to a wide range of data types and acquisition methods.

Publications

Publications (318)
Article
Full-text available
Background Cardiopulmonary exercise testing (CPET) provides a reliable and reproducible approach to measuring fitness in patients and diagnosing their health problems. However, the data from CPET consist of multiple time series that require training to interpret. Part of this training teaches the use of flow charts or nested decision trees to inter...
Preprint
Full-text available
Two major causes of death in the United States and worldwide are stroke and myocardial infarction. The underlying cause of both is thrombi released from ruptured or eroded unstable atherosclerotic plaques that occlude vessels in the heart (myocardial infarction) or the brain (stroke). Clinical studies show that plaque composition plays a more impor...
Chapter
The nature and quality of classroom instruction is highly correlated to teachers’ ability to rehearse effective teaching strategies. Utilizing research-based teaching practices increases teacher effectiveness, confidence, and retention along with improving student achievements. High-fidelity, AI-based simulated classroom systems enable teachers to...
Preprint
Full-text available
In this work, we propose a mutual information (MI) based unsupervised domain adaptation (UDA) method for the cross-domain nuclei segmentation. Nuclei vary substantially in structure and appearances across different cancer types, leading to a drop in performance of deep learning models when trained on one cancer type and tested on another. This doma...
Preprint
Full-text available
Exercise testing has been available for more than a half-century and is a remarkably versatile tool for diagnostic and prognostic information of patients for a range of diseases, especially cardiovascular and pulmonary. With rapid advancements in technology, wearables, and learning algorithm in the last decade, its scope has evolved. Specifically,...
Article
Background: Studies have shown that patients with ischemic stroke (IS) and concurrent COVID-19 have increased stroke severity. These analyses were limited by use of prepandemic era controls or by utilization of a sample from the early pandemic period when stroke care delivery was affected by lockdown. Studies on the severity of hemorrhagic stroke (...
Preprint
Background: Cardiopulmonary exercise testing (CPET) provides a reliable and reproducible approach to measuring fitness in patients and diagnosing their health problems. However, the data from CPET consist of multiple time series that require training to interpret. Part of this training teaches the use of flow charts or nested decision trees to inte...
Chapter
Effective and rapid detection of lesions in the Gastrointestinal (GI) tract plays a critical role in how fast gastroenterologist can respond to life-threatening diseases. Capsule Endoscopy (CE) has revolutionized traditional endoscopy procedure by allowing gastroenterologists visualize the entire GI tract non-invasively. Once the tiny capsule is sw...
Chapter
Video capsule endoscopy (VCE) provides a minimally invasive and highly effective screening mechanism for gut illness. Unfortunately, current methods require physicians to manually review the lengthy VCE recordings, which is both tedious and cost-prohibitive. Furthermore, available automation techniques are limited, and there is little publicly avai...
Preprint
Full-text available
A large body of research demonstrates how teachers' questioning strategies can improve student learning outcomes. However, developing new scenarios is challenging because of the lack of training data for a specific scenario and the costs associated with labeling. This paper presents a high-fidelity, AI-based classroom simulator to help teachers reh...
Preprint
Full-text available
High-fidelity, AI-based simulated classroom systems enable teachers to rehearse effective teaching strategies. However, dialogue-oriented open-ended conversations such as teaching a student about scale factor can be difficult to model. This paper presents a high-fidelity, AI-based classroom simulator to help teachers rehearse research-based mathema...
Chapter
Full-text available
Video capsule endoscope (VCE) is an emerging technology that allows examination of the entire gastrointestinal (GI) tract with minimal invasion. While traditional endoscopywith biopsy procedures are the gold standard for diagnosis of most GI diseases, they are limited by how far the scope can be advanced in the tract and are also invasive. VCE allo...
Preprint
Full-text available
Temporal activity localization in long videos is an important problem. The cost of obtaining frame level label for long Wireless Capsule Endoscopy (WCE) videos is prohibitive. In this paper, we propose an end-to-end temporal abnormality localization for long WCE videos using only weak video level labels. Physicians use Capsule Endoscopy (CE) as a n...
Preprint
Full-text available
Physicians use Capsule Endoscopy (CE) as a non-invasive and non-surgical procedure to examine the entire gastrointestinal (GI) tract for diseases and abnormalities. A single CE examination could last between 8 to 11 hours generating up to 80,000 frames which is compiled as a video. Physicians have to review and analyze the entire video to identify...
Chapter
The growth of the data science field requires better tools to understand such a fast-paced growing domain. Moreover, individuals from different backgrounds became interested in following a career as data scientists. Therefore, providing a quantitative guide for individuals and organizations to understand the skills required in the job market would...
Article
Motivation Genomic region sets summarize functional genomics data and define locations of interest in the genome such as regulatory regions or transcription factor binding sites. The number of publicly available region sets has increased dramatically, leading to challenges in data analysis. Results We propose a new method to represent genomic regi...
Conference Paper
Full-text available
The growth of the data science field requires better tools to understand such a fast-paced growing domain. Moreover, individuals from different backgrounds became interested in following a career as data scientists. Therefore, providing a quantitative guide for individuals and organizations to understand the skills required in the job market would...
Preprint
Full-text available
Background: Eoinophilic Esophagitis (EoE) is a chronic inflammatory condition diagnosed by >=15 eosinophils (Eos) per high-power field (HPF). There is no gold standard for clinical remission and Eo-associated metrics are poorly correlated with symptoms. Deep learning can be used to explore the relationships of tissue features with clinical response...
Preprint
Full-text available
The growth of the data science field requires better tools to understand such a fast-paced growing domain. Moreover, individuals from different backgrounds became interested in following a career as data scientists. Therefore, providing a quantitative guide for individuals and organizations to understand the skills required in the job market would...
Preprint
Full-text available
Advancement in digital pathology and artificial intelligence has enabled deep learning-based computer vision techniques for automated disease diagnosis and prognosis. However, WSIs present unique computational and algorithmic challenges. WSIs are gigapixel-sized, making them infeasible to be used directly for training deep neural networks. Hence, f...
Preprint
Motivation Genomic region sets summarize functional genomics data and define locations of interest in the genome such as regulatory regions or transcription factor binding sites. The number of publicly available region sets has increased dramatically, leading to challenges in data analysis. Results We propose a new method to represent genomic regi...
Preprint
Full-text available
In recent years, the availability of digitized Whole Slide Images (WSIs) has enabled the use of deep learning-based computer vision techniques for automated disease diagnosis. However, WSIs present unique computational and algorithmic challenges. WSIs are gigapixel-sized ($\sim$100K pixels), making them infeasible to be used directly for training d...
Article
Full-text available
Probe-based confocal laser endomicroscopy (pCLE) allows for real-time diagnosis of dysplasia and cancer in Barrett’s esophagus (BE) but is limited by low sensitivity. Even the gold standard of histopathology is hindered by poor agreement between pathologists. We deployed deep-learning-based image and video analysis in order to improve diagnostic ac...
Chapter
Hematoxylin and Eosin (H&E) stained Whole Slide Images (WSIs) are utilized for biopsy visualization-based diagnostic and prognostic assessment of diseases. Variation in the H&E staining process across different lab sites can lead to important variations in biopsy image appearance . These variations introduce an undesirable bias when the slides are...
Conference Paper
Eosinophilic Esophagitis (EoE) is an inflammatory esophageal disease which is increasing in prevalence. The diagnostic gold-standard involves manual review of a patient's biopsy tissue sample by a clinical pathologist for the presence of 15 or greater eosinophils within a single high-power field (400× magnification). Diagnosing EoE can be a cumbers...
Article
Objectives: Striking histopathological overlap between distinct but related conditions poses a disease diagnostic challenge. There is a major clinical need to develop computational methods enabling clinicians to translate heterogeneous biomedical images into accurate and quantitative diagnostics. This need is particularly salient with small bowel...
Preprint
Full-text available
Objective: Develop a deep learning-based methodology using the foundations of systems pathology to generate highly accurate predictive tools for complex gastrointestinal diseases, using celiac disease (CD) as a prototype. Design: To predict the severity of CD, defined by Marsh-Oberhuber classification, we used deep learning to develop a model based...
Preprint
Full-text available
Eosinophilic Esophagitis (EoE) is an inflammatory esophageal disease which is increasing in prevalence. The diagnostic gold-standard involves manual review of a patient's biopsy tissue sample by a clinical pathologist for the presence of 15 or greater eosinophils within a single high-power field (400x magnification). Diagnosing EoE can be a cumbers...
Preprint
Full-text available
Effective and rapid detection of lesions in the Gastrointestinal tract is critical to how promptly gastroenterologist can respond to some life-threatening diseases. Wireless Capsule Endoscopy (WCE) has revolutionized traditional endoscopy procedure by allowing gastroenterologists visualize the entire GI tract non-invasively. Once the tiny capsule i...
Preprint
Full-text available
Machine learning techniques applied to the Natural Language Processing (NLP) component of conversational agent development show promising results for improved accuracy and quality of feedback that a conversational agent can provide. The effort required to develop an educational scenario specific conversational agent is time consuming as it requires...
Chapter
Full-text available
Analyzing the ever-increasing volume of posts on social media sites, such as Facebook and Twitter requires improved information processing methods for profiling authorship. Document classification is central to this task, but the performance of traditional supervised classifiers has degraded as the volume of social media has increased. This paper a...
Conference Paper
One of the greatest obstacles in the adoption of deep neural networks for new medical applications is that training these models typically require a large amount of manually labeled training samples. In this body of work, we investigate the semi-supervised scenario where one has access to large amounts of unlabeled data and only a few labeled sampl...
Article
Full-text available
The gold standard of histopathology for the diagnosis of Barrett's esophagus (BE) is hindered by inter-observer variability among gastrointestinal pathologists. Deep learning-based approaches have shown promising results in the analysis of whole-slide tissue histopathology images (WSIs). We performed a comparative study to elucidate the characteris...
Preprint
Objectives: Striking histopathological overlap between distinct but related conditions poses a significant disease diagnostic challenge. There is a major clinical need to develop computational methods enabling clinicians to translate heterogeneous biomedical images into accurate and quantitative diagnostics. This need is particularly salient with s...
Chapter
Full-text available
Online propaganda is central to the recruitment strategies of extremist groups and in recent years these efforts have increasingly extended to women. To investigate Islamic State’s approach to targeting women in their online propaganda and uncover implications for counterterrorism, we rely on text mining and natural language processing (NLP). Speci...
Preprint
Full-text available
Video capsule endoscope (VCE) is an emerging technology that allows examination of the entire gastrointestinal (GI) tract with minimal invasion. While traditional endoscopy with biopsy procedures are the gold standard for diagnosis of most GI diseases, they are limited by how far the scope can be advanced in the tract and are also invasive. VCE all...
Preprint
Perioperative data are essential to investigating the causes of adverse surgical outcomes. In some low to middle income countries, these data are computationally inaccessible due to a lack of digitization of surgical flowsheets. In this paper, we present a deep image segmentation approach using a U-Net architecture that can detect hand-drawn symbol...
Article
Full-text available
Image classification is central to the big data revolution in medicine. Improved information processing methods for diagnosis and classification of digital medical images have shown to be successful via deep learning approaches. As this field is explored, there are limitations to the performance of traditional supervised classifiers. This paper out...
Preprint
Full-text available
Image classification is central to the big data revolution in medicine. Improved information processing methods for diagnosis and classification of digital medical images have shown to be successful via deep learning approaches. As this field is explored, there are limitations to the performance of traditional supervised classifiers. This paper out...
Preprint
Full-text available
Deep convolutional neural networks (CNNs) have been successful for a wide range of computer vision tasks including image classification. A specific area of application lies in digital pathology for pattern recognition in tissue-based diagnosis of gastrointestinal (GI) diseases. This domain can utilize CNNs to translate histopathological images into...
Article
Full-text available
Analyzing the ever increasing volume of posts on social media sites such as Facebook and Twitter requires improved information processing methods for profiling authorship. Document classification is central to this task, but the performance of traditional supervised clas-sifiers has degraded as the volume of social media has increased. This paper a...
Preprint
Full-text available
Analyzing the ever-increasing volume of posts on social media sites such as Facebook and Twitter requires improved information processing methods for profiling authorship. Document classification is central to this task, but the performance of traditional supervised classifiers has degraded as the volume of social media has increased. This paper ad...
Preprint
Full-text available
Existing simulations designed for cultural and interpersonal skill training rely on pre-defined responses with a menu option selection interface. Using a multiple-choice interface and restricting trainees' responses may limit the trainees' ability to apply the lessons in real life situations. This systems also uses a simplistic evaluation model, wh...
Article
The most severe form of kidney disease, End-Stage Renal Disease (ESRD) is treated with various forms of dialysis – artificial blood cleansing. Dialysis patients suffer many health burdens including high mortality and hospitalization rates, and symptomatic anemia: a low red blood cell count as indicated by a low hemoglobin (Hgb) level. ESRD-induced...