Jingjing Gao

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BACKGROUND AND OBJECTIVE Morphologic variations of disease are often linked to underlying molecular events and patient outcome, suggesting that quantitative morphometric analysis may provide further insight into disease mechanisms. In this paper a methodology for the subclassification of disease is developed using image analysis techniques. Morphologic(More)
Markov random field (MRF) model is an effective method for brain tissue classification, which has been applied in MR image segmentation for decades. However, it falls short of the expected classification in MR images with intensity inhomogeneity for the bias field is not considered in the formulation. In this paper, we propose an interleaved method joining(More)
OBJECTIVES We present SHARE, a new system for statistical health information release with differential privacy. We present two case studies that evaluate the software on real medical datasets and demonstrate the feasibility and utility of applying the differential privacy framework on biomedical data. MATERIALS AND METHODS SHARE releases statistical(More)
Pathologic review of tumor morphology in histologic sections is the traditional method for cancer classification and grading, yet human review has limitations that can result in low reproducibility and inter-observer agreement. Computerized image analysis can partially overcome these shortcomings due to its capacity to quantitatively and reproducibly(More)
We propose a new approach to evaluating agreement between two observers making binary observations on a set of subjects. This approach compares the probability of disagreement (or discordance) between the observers to the probability of disagreement between replicated observations made by the same observer on the same subject. We consider two situations:(More)
Multimodal, multiscale data synthesis is becoming increasingly critical for successful translational biomedical research. In this letter, we present a large-scale investigative initiative on glioblastoma, a high-grade brain tumor, with complementary data types using in silico approaches. We integrate and analyze data from The Cancer Genome Atlas Project on(More)
Hospital readmissions depend on numerous factors. Automated risk calculation using electronic health record (EHR) data could allow targeting care to prevent them. EHRs usually are incomplete with respect to data relevant to readmissions prediction. Lack of standard data representations in EHRs restricts generalizability of predictive models. We propose(More)
BACKGROUND Algorithm evaluation provides a means to characterize variability across image analysis algorithms, validate algorithms by comparison with human annotations, combine results from multiple algorithms for performance improvement, and facilitate algorithm sensitivity studies. The sizes of images and image analysis results in pathology image analysis(More)