Related papers: Boundary detection in disease mapping studies
Lung cancer is a leading cause of cancer-related deaths worldwide. The spread of the disease from its primary site to other parts of the lungs, known as metastasis, significantly impacts the course of treatment. Early identification of…
This paper presents an algorithm which aims to assist the radiologist in identifying breast cancer at its earlier stages. It combines several image processing techniques like image negative, thresholding and segmentation techniques for…
We apply a linear Bayesian model to seismic tomography, a high-dimensional inverse problem in geophysics. The objective is to estimate the three-dimensional structure of the earth's interior from data measured at its surface. Since this…
The prevalence of dementia is set to explode throughout the 21st century. This trend has already started in developed countries and will continue to place heavy pressures on both public health and social care services across the world. No…
In public health, various data are rigorously collected and published with open access. These data reflect the environmental and non-environmental characteristics of heterogeneous neighborhoods in cities. In the present study, we aimed to…
Datasets are rarely a realistic approximation of the target population. Say, prevalence is misrepresented, image quality is above clinical standards, etc. This mismatch is known as sampling bias. Sampling biases are a major hindrance for…
This paper describes an interdisciplinary approach to geometry modeling of geospatial boundaries. The objective is to extract surfaces from irregular spatial patterns using differential geometry and obtain coherent directional predictions…
Computer-aided diagnosis (CAD), a vibrant medical imaging research field, is expanding quickly. Because errors in medical diagnostic systems might lead to seriously misleading medical treatments, major efforts have been made in recent years…
Several biomarkers are hypothesized to indicate early stages of Alzheimer's disease, well before the cognitive symptoms manifest. Their precise relations to the disease progression, however, is poorly understood. This lack of understanding…
In many clinical and epidemiological studies, collecting longitudinal measurements together with time-to-event outcomes is essential. Accurately estimating the association between longitudinal markers and event risks, as well as identifying…
Hotspot detection aims at identifying subgroups in the observations that are unexpected, with respect to the some baseline information. For instance, in disease surveillance, the purpose is to detect sub-regions in spatiotemporal space,…
Mathematical and computational modeling approaches are increasingly used as quantitative tools in the analysis and forecasting of infectious disease epidemics. The growing need for realism in addressing complex public health questions is…
Computer-aided detection has been a research area attracting great interest in the past decade. Machine learning algorithms have been utilized extensively for this application as they provide a valuable second opinion to the doctors.…
One of the deadliest cancers, lung cancer necessitates an early and precise diagnosis. Because patients have a better chance of recovering, early identification of lung cancer is crucial. This review looks at how to diagnose lung cancer…
Background: Bayesian Networks (BNs) are probabilistic graphical models that leverage Bayes' theorem to portray dependencies and cause-and-effect relationships between variables. These networks have gained prominence in the field of health…
Software is highly contextual. While there are cross-cutting `global' lessons, individual software projects exhibit many `local' properties. This data heterogeneity makes drawing local conclusions from global data dangerous. A key research…
Bayesian change-point detection, together with latent variable models, allows to perform segmentation over high-dimensional time-series. We assume that change-points lie on a lower-dimensional manifold where we aim to infer subsets of…
Spatially-explicit estimates of population density, together with appropriate estimates of uncertainty, are required in many management contexts. Density Surface Models (DSMs) are a two-stage approach for estimating spatially-varying…
An interesting statistical problem is to find regions where some studied process exceeds a certain level. Estimating such regions so that the probability for exceeding the level in the entire set is equal to some predefined value is a…
Surgical automation has the potential to enable increased precision and reduce the per-patient workload of overburdened human surgeons. An effective automation system must be able to sense and map subsurface anatomy, such as tumors,…