Computer-Aided Prognosis of Age-Related Macular Degeneration
Advanced form of age-related macular degeneration (AMD) is a major health burden that can lead to irreversible vision loss in the elderly population. For early preventative interventions, there is a lack of effective tools to predict the prognosis outcome of advanced AMD because of the similar visual appearance of retinal image scans in the early stage and the variability of prognosis paths among patients. The existing prognosis models have several limitations: First, previous studies assume constant time intervals between doctor visits; however, in real world clinical settings, the visits may happen at irregular time intervals. The assumption of constant time intervals will lead to over-optimistic prediction results on specific training data sets while failing to produce generalizable results on new patient data sets. Second, current studies only predict one form of advanced AMD form at a time. Third, computer-based prognosis results are typically not validated on new patients and therefore, it is difficult to evaluate the generalizability of the proposed approaches. Lastly, there is a lack of interpretability of the models and explainability of how a computer-based prognosis determination has been made. The overall objective for this project is to design, develop, and evaluate AMD prognosis prediction models that can detect most relevant images containing AMD biomarkers, manage unevenly spaced sequential optical coherence tomography (OCT) images and predict all advanced AMD forms that can help with the interpretation and explainability of computer-aided prognosis models.
Primary Care Project
Primary care clinics play a vital role in identifying disease at early stages, providing continuous comprehensive care to the patients, and overall providing better healthcare facilities. In such primary care settings, patient-physician communication plays a crucial role in patient satisfaction and overall health outcomes. With advances in technology, primary health care centers have adopted Electronic Health Record (EHR) systems in the clinical care settings. Although EHRs have been adopted to improve medication management and documentation of patient health records, EHRs seem to affect negative effects on the physicians. The overall communication between the patient and the physician seems to be affected. This project aims to understand the impact of healthcare technology on both patients and doctors in primary clinical care settings. The current stage of the project includes identifying physician's gaze using computer vision and deep learning techniques. The data used in the project are recorded video interactions between the physician and the patient.
Computer-aided Diagnosis for Lung Cancer
The Lung Nodule Database Consortium (LIDC) is a joint initiative among five institutions providing a dataset for researchers to conduct analysis and develop techniques to advance the state-of-art computer-aided diagnosis (CADx) and detection (CADe) of lung nodules using Computed Tomography (CT) scans. The dataset contains 1,018 patient CT series in which 2,669 nodules are identified, outlined and rated by up to four radiologists. Radiologists provide 5-point scale ratings for 8 intermediate semantic characteristics and a malignancy rating. The complex structure of this dataset provides opportunities to conduct compelling research in Image Processing and Analysis, Machine Learning, and Data Mining, solving problems relevant beyond the medical domain.
Video Segmentation and Annotation
While performing action/activity recognition with medical and biological video data, we encountered common challenges with both obtaining accurate class labels for an entire video and segmenting the video into clips representing a single activity. Our goal is to develop methods that improve the accuracy and reduce the effort for both tasks. Current research is focused on applying signal process methods to temporal data in reference to the subject(s) of the video.
The Integrated Radiology Image Search (IRIS) Engine Project
Radiology teaching files serve as a reference in the diagnosis process and as a learning resource for radiology residents. There are many public teaching file data sources available online; private in-house teaching file repositories are further maintained in most hospitals. Teaching files include both text and images with structural variations even within the same data repository. Moreover, the native interfaces in existing repository have a very limited search capability. The Integrated Radiology Image Search (IRIS) engine is designed to combine publicly available data sources and in-house teaching files in a unified resource. IRIS provide text-based, image-based, and hybrid (text + image) search functionality.
MedIX: Research Experience for Undergraduates (REU) Program
The Medical Informatics (MedIX) program is a National Science Foundation REU site hosted by two interdisciplinary laboratories: the Medical Informatics Laboratory at DePaul University and the Imaging Research Institute at the University of Chicago. The main objectives of the MedIX REU program are to encourage talented undergraduates to pursue graduate education and to expose students to interdisciplinary research, especially at the border of information technology and medicine.
For more information, please visit the MedIX REU site here.
Identification of Gene Function Using Image Analytics
A challenge in neuroscience is understanding the genetic bases of behavior; traditionally, the nematode Caenorhabditis elegans is used as a model organism for neural studies due to its simple neural structure and well known connectome. Our study aims to identify genes that modulate food related behaviors in nematodes by quantifying behavioral differences in mutant strains. In order to empirically quantify nematode behavior, we have developed methods for recording and tracking nematodes over long periods of time, as well as algorithms for extracting and analyzing information from the observational data.
Bridging the Gap between Human and Computer Interpretation of Similarity in the Medical Domain
Focused on reducing the semantic gap by investigating computer-based similarity measures and image features that are close to the human perception of similarity and encoded the visual content of an image similarly to the human vision.
Information Extraction from Radiology Reports for the Purpose of Automatic Semantic Annotation of Medical Images
Proposed the development of a full-featured and scalable IE system capable of converting free-text radiology reports to a structured format.
Computer-aided Diagnosis for Breast Masses
Proposed: 1) a novel automatic mass segmentation method for identifying the contour (boundary) of a mass from a suspicious region (ROI) in a mammogram; 2) A multiple segmentation approach which builds multiple weak segmentors for each ROI image by applying a set of different image enhancements for mass segmentation; and 3) An ensemble approach where multiple base-level classifiers are built as experts from different perspectives to predicate the class probabilities; then, utilizing another learning algorithm, a meta-level classifier combines the diagnoses to generate the final diagnosis for a suspicious mass.
Approached a patent database and its citations with theoretical models applied from statistical analysis and physics. Using the clustering techniques of complex networks, we searched for patterns of relationships between patents of different categories.
Argonne National Laboratory (ANL) funded research work within the context of the Department of Homeland Security (DHS) microbial forensics programs toward the development of statistically-based experimental designs, microarray image analysis and decision tools for the analysis of genotyping and single nucleotide polymorphism microarrays.