Grid Databases for Shared Image Analysis in the MammoGrid Project

The MammoGrid project aims to prove that Grid infrastructures can be used for collaborative clinical analysis of database-resident but geographically distributed medical images. This requires: a) the provision of a clinician-facing front-end workstation and b) the ability to service real-world clinician queries across a distributed and federated database. The MammoGrid project will prove the viability of the Grid by harnessing its power to enable radiologists from geographically dispersed hospitals to share standardized mammograms, to compare diagnoses (with and without computer aided detection of tumours) and to perform sophisticated epidemiological studies across national boundaries. This paper outlines the approach taken in MammoGrid to seamlessly connect radiologist workstations across a Grid using an "information infrastructure" and a DICOM-compliant object model residing in multiple distributed data stores in Italy and the UKComment: 10 pages, 5 figure

Markov chain monte Carlo methods in Bayesian Inference

The Markov Chain Monte-Carlo (MCMC) born in early 1950s has recently aroused great interest among statisticians, particularly researchers working in image analysis, discrete optimization, neural networks, genetic sequencing and other related Eelds. Recent theoretical achievements in resampling procedures provide a new perspective for handling errors in Bayesian inference, which treats all unknowns as random variables. The unknowns include uncertainties in the model such as fixed effects, random effects, unobserved indicator variables and missing data. Only in few cases, the posterior distribution is in standard analytic form. In most other models like generalized linear models, mixture models, epidemiological models and survival analysis, the exact analytic Bayesian inference is impossible. This paper surveys some of the recent advances in this area that allows exact Bayesian computation using simulations and discusses some applications to biomedical data

Disease surveillance and patient care in remote regions: an exploratory study of collaboration among healthcare professionals in Amazonia

The development and deployment of information technology, particularly mobile tools, to support collaboration between different groups of healthcare professionals has been viewed as a promising way to improve disease surveillance and patient care in remote regions. The effects of global climate change combined with rapid changes to land cover and use in Amazonia are believed to be contributing to the spread of vector-borne emerging and neglected diseases. This makes empowering and providing support for local healthcare providers all the more important. We investigate the use of information technology in this context to support professionals whose activities range from diagnosing diseases and monitoring their spread to developing policies to deal with outbreaks. An analysis of stakeholders, their roles and requirements, is presented which encompasses results of fieldwork and of a process of design and prototyping complemented by questionnaires and targeted interviews. Findings are analysed with respect to the tasks of diagnosis, training of local healthcare professionals, and gathering, sharing and visualisation of data for purposes of epidemiological research and disease surveillance. Methodological issues regarding the elicitation of cooperation and collaboration requirements are discussed and implications are drawn with respect to the use of technology in tackling emerging and neglected diseases