Daniela Stan Raicu

DePaul University · College of Computing and Digital Media

Extracting scientific facts from unstructured text is difficult due to challenges specific to the complexity of the scientific named entities and relations to be extracted. This problem is well illustrated through the extraction of polymer names and their properties. Even in the cases where the property is a temperature, identifying the polymer nam...

INTRODUCTION: Detecting and identifying patterns in chest X-ray images of Covid-19 patients are important tasks for understanding the disease and for making differential diagnosis. OBJECTIVES: The purpose of this work is to develop a technique for detecting biomarkers of four possible conditions in chest X-rays, and study the patterns arising from...

Detecting and identifying patterns in chest X-ray images of Covid-19 patients are important tasks for understanding the disease and for making differential diagnosis. Given the relatively small number of available Covid-19 X-ray images and the need to make progress in understanding the disease, we propose a transfer learning technique applied to a...

Patient-centered communication is crucial in the clinical encounter. Previous studies on patient satisfaction have focused on nonverbal cues and demographics of the patient separately; the integrated influence of both aspects is yet to be explored. This study aims to build a model to learn the quantitative relationship among nonverbal behaviors suc...

Computer-aided Diagnosis (CAD) systems have long aimed to be used in clinical practice to help doctors make decisions by providing a second opinion. However, most machine learning based CAD systems make predictions without explicitly showing how their predictions were generated. Since the cognitive process of the diagnostic imaging interpretation i...

Radiology teaching file repositories contain a large amount of information about patient health and radiologist interpretation of medical findings. Although valuable for radiology education, the use of teaching file repositories has been hindered by the ability to perform advanced searches on these repositories given the unstructured format of the...

Miscommunication of diagnostic uncertainty can deeply affect the quality of treatment a patient receives. A standardized quantification based on the language used in medical reports is a solution for gaining clarity about the amount of uncertainty an author intended to convey. We use natural language processing techniques to create a dictionary of...

Computer-Aided Diagnosis systems have been used as second readers in the medical imaging diagnostic process. In this study, we aim to identify cases that are hard to diagnose and lead to interpretation variability among medical experts. We propose a combination of image features and advanced machine learning classifiers to predict the degree of mal...

In this article, we describe DePaul University's summer data science academy for Chicago Public School students which was, in part, funded through the SIGKDD Impact Award program in 2018. The goal of the academy was to increase awareness about data science among high school students. The program specifically aimed to broaden participation of underr...

Vast amounts of clinical and biomedical research data are produced daily. These data can help enable data driven healthcare through novel biomedical discoveries, improved diagnostics processes, epidemiology, and education. However, finding, and gaining access to these data and relevant metadata that are necessary to achieve these goals remains a ch...

Age-related macular degeneration (AMD) is a degenerative aging disorder, which can lead to irreversible vision loss in older individuals. The emergence of clinical applications of retinal hyper-spectral imaging provides a unique opportunity to capture important spectral signatures, with the potential to enhance the molecular diagnosis of retinal di...

We explore several oversampling techniques for an imbalanced multi-label classification problem, a setting often encountered when developing models for Computer-Aided Diagnosis (CADx) systems. While most CADx systems aim to optimize classifiers for overall accuracy without considering the relative distribution of each class, we look into using synt...

Teaching files are widely used by radiologists in the diagnostic process and for student education. Most hospitals maintain an active collection of teaching files for internal purposes, but many teaching files are also publicly available online, some linked to secondary sources. However, public sources offer very limited (and ad-hoc) search capabil...

Background: Suicide is an alarming public health problem accounting for a considerable number of deaths each year worldwide. Many more individuals contemplate suicide. Understanding the attributes, characteristics, and exposures correlated with suicide remains an urgent and significant problem. As social networking sites have become more common, u...

Background: This study aimed to determine whether publicized hospital rankings can be used to predict surgical outcomes. Methods: Patients undergoing one of nine surgical procedures were identified, using the Healthcare Cost and Utilization Project State Inpatient Database for Florida and New York 2011-2013 and merged with hospital data from the...

Background: Our objective was to determine the hospital resources required for low-volume, high-quality care at high-volume cancer resection centers. Methods: Patients who underwent esophageal, pancreatic, and rectal resection for malignancy were identified using Healthcare Cost and Utilization Project State Inpatient Database (Florida and Calif...

Many efforts to broaden participation of underreppresented youth in STEM fields involve the implementation of short-term, informal learning engagements to provide exposure to STEM activity (e.g., one-day workshops, one-week camps, summer-long programs). To attract youth who may not have an existing interest in STEM, one common approach is to situat...

The nematode Caenorhabditis elegans explores the environment using a combination of different movement patterns, which include straight movement, reversal, and turns. We propose to quantify C. elegans movement behavior using a computer vision approach based on run-length encoding of step-length data. In this approach, the path of C. elegans is enco...

Computer-Aided Diagnosis (CAD) systems can provide a second opinion for either identifying suspicious regions on a medical image or predicting the degree of malignancy for a detected suspicious region. To develop a predictive model, CAD systems are trained on low-level image features extracted from image data and the class labels acquired through r...

The nematode Caenorhabditis elegans provides a unique opportunity to interrogate the neural basis of behavior at single neuron resolution. In C. elegans, neural circuits that control behaviors can be formulated based on its complete neural connection map, and easily assessed by applying advanced genetic tools that allow for modulation in the activi...

Path Graphs for the 6 Videos Presented in Fig 9. (EPS)

Mean step length vs. reversals. A: Mean step length of a tph-1 mutant on food. B: Reversals of the same tph-1 mutant on food. The size of the mean step length shows an inversed relationship with the frequency of reversals over a period of time. (EPS)

Large conductance, calcium-activated BK channels regulate many important physiological processes, including smooth muscle excitation, hormone release and synaptic transmission. The biological roles of these channels hinge on their unique ability to respond synergistically to both voltage and cytosolic calcium elevations. Because calcium influx is m...

In this paper, we present the results of our investigation on identifying the optimal segmentor(s) from an ensemble of weak segmentors, used in a Computer-Aided Diagnosis (CADx) system which classifies suspicious masses in mammograms as benign or malignant. This is an extension of our previous work, where we used various parameter settings of image...

Analysis of medical images by radiologists is often a time consuming and costly process. Computer Aided diagnosis (CAD) systems can be used to provide second opinion and assist radiologists in diagnosis. Crowdsourcing has been a tool used by many domains in order to generate fast and cheap labels. However, exposing medical images to a large crowd i...

Medical imaging technology has always provided radiologists with the opportunity to view and keep records of anatomy of the patient. With the development of machine learning and intelligent computing, these images can be used to create Computer-Aided Diagnosis (CAD) systems, which can assist radiologists in analyzing image data in various ways to p...

We analyze the importance of shape features for predicting spiculation ratings assigned by radiologists to lung nodules in computed tomography (CT) scans. Using the Lung Image Database Consortium (LIDC) data and classification models based on decision trees, we demonstrate that the importance of several shape features increases disproportionately r...

Computer-aided diagnosis systems can provide additional opinions that serve as an aid to radiologists in the early detection of lung nodules. Previous CAD models have relied on radiologist-delineated contours to extract image features and classify lung nodules into semantic ratings. Manually creating these contours can be time-consuming and expensi...

Computer-aided diagnosis systems can play an important role in lowering the workload of clinical radiologists and reducing costs by automatically analyzing vast amounts of image data and providing meaningful and timely insights during the decision making process. In this paper, we present strategies on how to better manage the limited time of clini...

The workload associated with the daily job of a clinical radiologist has been steadily increasing as the volume of the archived and the newly acquired images grows. Computer-aided diagnostic systems are becoming an indispensable tool in automating image analysis and providing preliminary diagnosis that can help guide radiologist's decisions. In thi...

The problem of classifying samples for which there is no definite label is a challenging one in which multiple annotators will provide a more certain input for a classifier. Unlike most of active learning scenarios that require identifying which images to be annotated, we explore how many annotations can potentially be used per instance (one annota...

Computer-aided diagnosis (CAD) can be used as "second readers" in the imaging diagnostic process. Typically to create a CAD system, the region of interest (ROI) has to be first detected and then delineated. This can be done either manually or automatically. Given that manually delineating ROIs is a time consuming and costly process, we propose a CA...

Evaluation for probabilistic multiclass systems has predominately been done by converting data into binary classes. While effective in quantifying the classifier performance, binary evaluation causes a loss in ability to distinguish between individual classes. We report that the evaluation of multiclass probabilistic classifiers can be quantified b...

In this paper we propose to use gesture recognition approaches to track a human hand in 3D space and, without the use of special clothing or markers, be able to accurately generate code for training an industrial robot to perform the same motion. The proposed hand tracking component includes three methods: a color-thresholding model, naïve Bayes an...

Evaluating the success of computer-aided decision support systems depends upon a reliable reference standard, a ground truth. The ideal gold standard is expected to result from the marking, labeling, and rating by domain experts of the image of interest. However experts often disagree, and this lack of agreement challenges the development and evalu...

In computer-aided diagnosis (CAD), having an accurate ground truth is critical. However, the number of databases containing medical images with diagnostic information is limited. Using pulmonary computed tomography (CT) scans, we develop a content-based image retrieval (CBIR) approach to exploit the limited images with diagnostically labeled data i...

The effectiveness and efficiency of content-based image retrieval (CBIR) can be improved by determining an optimal combination of image features to use in determining similarity between images. This combination of features can be optimized using a genetic algorithm (GA). Although several studies have used genetic algorithms to refine image features...

Traditionally, image studies evaluating the effectiveness of computer-aided diagnosis (CAD) use a single label from a medical expert compared with a single label produced by CAD. The purpose of this research is to present a CAD system based on Belief Decision Tree classification algorithm, capable of learning from probabilistic input (based on intr...

When examining Computed Tomography (CT) scans of lungs for potential abnormalities, radiologists make use of lung nodule's semantic characteristics during the analysis. Computer-Aided Diagnostic Characterization (CADc) systems can act as an aid - predicting ratings of these semantic characteristics to aid radiologists in evaluating the nodule and p...

In reading Computed Tomography (CT) scans with potentially malignant lung nodules, radiologists make use of high level information (semantic characteristics) in their analysis. Computer-Aided Diagnostic Characterization (CADc) systems can assist radiologists by offering a "second opinion"--predicting these semantic characteristics for lung nodules....

Evaluating the success of prediction and retrieval systems depends upon a reliable reference standard, a ground truth. The ideal gold standard is expected to result from the marking, labeling, and rating of domain experts. However experts disagree and lack of agreement challenges the development and evaluation of image-based feature prediction. Thi...

Producing consistent segmentations of lung nodules in CT scans is a persistent problem of image processing algorithms. Many hard-segmentation approaches are proposed in the literature, but soft segmentation of lung nodules remains largely unexplored. In this paper, we propose a classification-based approach based on pixel-level texture features tha...

Classification of a suspicious mass (region of interest, ROI) in a mammogram as malignant or benign may be achieved using mass shape features. An ensemble system was built for this purpose and tested. Multiple contours were generated from a single ROI using various parameter settings of the image enhancement functions for the segmentation. For each...

Undergraduate research is becoming increasingly common in colleges and universities, and, to support this, there is a need to have best practices and forums for promoting exchange of ideas. In particular, a working group at a recent National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Research Experiences for Un...

This paper proposes to build multiple segmentations for identifying mass contours for a suspicious mass in a mammogram. In this study, by using various parameter settings of the image enhancement functions, we perform multiple segmentations for each suspicious mass (region of interest (ROI)), and multiple mass contours are generated. Each of such s...

We are investigating various techniques to improve the quality of Content-Based Image Retrieval(CBIR) for computed-tomography(CT) scans of lung nodules. Previous works have used linear regression models1 and artificial neural networks(ANN)6 to predict the similarity between two nodules. This paper expands upon this work incorporating contextual inf...

A decision tree ensemble classifier is a machine learning technique in which several decision trees are joined in an effort to improve the classification accuracy of a prediction model. In this paper, a three-level decision tree ensemble is proposed in the classification of chronic fatigue syndrome (CFS). While existing CFS classification models ar...

Evaluating the success of computer-aided decision support systems depends upon a reliable reference standard, a ground truth. The ideal gold standard is expected to result from the marking, labeling, and rating by domain experts of the image of interest. However experts often disagree, and this lack of agreement challenges the development and evalu...

Normalized Information Distance, based on Kolmogorov complexity, is an emerging metric for image similarity. It is approximated by the Normalized Compression Distance (NCD) which generates the relative distance between two strings by using standard compression algorithms to compare linear strings of information. This relative distance quantifies th...

Previous research has been done to classify different tissues/organs of interest present in medical images, in particular in Computed Tomography (CT) images. Most of the research used the anatomical structure present in the images in order to classify the tissues. In this paper, instead of using the anatomical structure, we propose a pixel-based te...

Microarray technology allows biologists to test for a large number of DNA sequences with a single test. Each microarray may consist of hundreds or thousands of probes to test for specific sequences. These devices are typ ically developed to target a specific DNA type. A proposed universal microarray that use s a few hundred randomly selected probes...

There is considerable research in the field of content-based medical image retrieval; however, few of the current systems investigate the relationship between the radiologists' visual impression of image similarity and the computer calculated content-based similarity. Furthermore, those research studies that investigate these relationships analyze...

There is considerable research in the field of content-based image retrieval (CBIR); however, few of the current systems incorporate radiologists' visual impression of image similarity. Our objective is to bridge the semantic gap between radiologists' ratings and image features. We have been developing a conceptual-based similarity model derived fr...

Computer-aided diagnostic characterization (CADc) aims to support medical imaging decision making by objectively rating the radiologists' subjective, perceptual opinions of visual diagnostic characteristics of suspicious lesions. This research uses the publicly available Lung Image Database Consortium (LIDC) collection of radiologists' outlines of...

This paper presents a novel, edge-based segmentation method for identifying the mass contour (boundary) for a suspicious mass region (Region of Interest (ROI)) in a mammogram. The method first applies a contrast stretching function to adjust the image contrast, then uses a filtering function to reduce image noise. Next, for each pixel in a ROI, the...

The segmentation of medical images is challenging because a ground truth is often not available. Computer-Aided Detection (CAD) systems are dependent on ground truth as a means of comparison; however, in many cases the ground truth is derived from only experts' opinions. When the experts disagree, it becomes impossible to discern one ground truth....

Ideally, an image should be reported and interpreted in the same way (e.g., the same perceived likelihood of malignancy) or similarly by any two radiologists; however, as much research has demonstrated, this is not often the case. Various efforts have made an attempt at tackling the problem of reducing the variability in radiologists’ interpretatio...

In reading CT scans with potentially malignant lung nodules, radiologists make use of high level information (semantic characteristics) in their analysis. CAD systems can assist radiologists by offering a "second opinion" -predicting these semantic characteristics for lung nodules. In our previous work, we developed such a CAD system, training and...

While the advances in Computed Tomography (CT) technology allow better detection of pulmonary nodules by generating higher-resolution images, the new technology also generates more individual transverse reconstructions. As a result, the efficiency and accuracy of the radiologists interpreting these images is reduced. Double reading by two human obs...

This paper uses an ensemble of classifiers and active learning strategies to predict radiologists’ assessment of the nodules of the Lung Image Database Consortium (LIDC). In particular, the paper presents machine learning classifiers that model agreement among ratings in seven semantic characteristics: spiculation, lobulation, texture, sphericity,...

We propose a solution for automatic classification of lung nodules in an environment with heterogeneous computed tomography (CT) acquisition parameters. Such a classification system needs to take into account the differences in CT acquisition parameters used when obtaining and processing each medical image. Using analysis of variance (ANOVA), our c...

Several research studies have shown that interpretation performance varies greatly among radiologists. One specific example is the Lung Image Database Consortium LIDC dataset. Although it was created to serve as an international research resource for the development and evaluation of computer-aided diagnosis CAD algorithms, out of 149 distinct nodu...

This paper presents an ensemble learning approach for classifying masses in mammograms as malignant or benign by using Breast Image Report and Data System (BI-RADS) descriptors. We first identify the most important BI-RADS descriptors based on the information gain measure. Then we quantize the fine-grained categories of those descriptors into coars...

Clinical narratives, such as radiology and pathology reports, are commonly available in electronic form. However, they are also commonly entered and stored as free text. Knowledge of the structure of clinical narratives is necessary for enhancing the productivity of healthcare departments and facilitating research. This study attempts to automatica...

This paper presents a comparison study between 10 automatic and six interactive methods for liver segmentation from contrast-enhanced CT images. It is based on results from the "MICCAI 2007 Grand Challenge" workshop, where 16 teams evaluated their algorithms on a common database. A collection of 20 clinical images with reference segmentations was p...

This paper presents a successful model for undergraduate research where student participants work on interdisciplinary research projects; in our case, at the frontier between computer science and medicine. Students are part of research teams comprised of other undergraduates, graduate students, faculty and medical experts, participate in profession...

This paper presents a successful model for undergraduate research where student participants work on interdisciplinary research projects; in our case, at the frontier between computer science and medicine. Students are part of research teams comprised of other undergraduates, graduate students, faculty and medical experts, participate in profession...

Using computer-calculated features to characterize the shape of suspicious lesions aims to assist the diagnosis of pulmonary nodules; however, these computerized features must agree with radiologists' interpretation of these visual, diagnostic characteristics. In the Lung Image Database Consortium (LIDC), there exists strong disagreement among the...

Most Computer-Aided Diagnosis (CAD) research studies are performed using a single type of Computer Tomography (CT) scanner and therefore, do not take into account the effect of differences in the imaging acquisition scanner parameters. In this paper, we present a study on the effect of the CT parameters on the low-level image features automatically...