Welcome to the CBIIT Speaker Series Wiki
The need for decision support systems in radiology is growing given the dramatic increase in imaging utilization, intensity and workload. Dr. Summers' laboratory at NIH focuses on the application of advanced image processing and machine learning techniques to provide decision support for radiology image interpretation. As a body radiologist and CT subspecialist, Dr. Summers has chosen to focus his research on the development of decision support for thoracoabdominal CT image interpretation.
In this talk, Dr. Summers will discuss his laboratory's approach to full automation of body CT interpretation. In the last three years, his laboratory has made substantial progress towards this goal. Topics will include fully-automated detection and segmentation of major body organs and their lesions, including spine and spine lesions and lymphadenopathy. Validation results will be presented. Dr. Summers will describe potential unrecognized benefits of fully-automated quantitation on routine body CT scans without the need for additional radiation exposure. He will also discuss the impact of advances in deep learning to radiology image analysis.
PhenoDB is a web-based tool developed for the collection, storage and analysis of phenotype data, as well as interpretation of exome and genome data in the context of phenotype data. It has it own taxonomy and links to OMIM for disease terms. There is a single center version that allow identifiers and includes only the phenotype and analysis module (http://phenodb.org) and a tool for larger studies that also includes a sample module and an ELSI module for storage and review of consents (http://researchphenodb.net). Both are freely available for download; http://phenodb.org can be used by individual users to try it out (it is toggled to have only deidentified data). PhenoDB has been in use for the Baylor-Hopkins Center for Mendelian Genomics since March 2012 and holds information on over 5000 individuals from ~3000 families. It has proved efficient and effective in novel disease gene discovery. It can also be used for a laboratory or clinic.
This presentation will cover current status of the SEER program and review opportunities for use of the data in support of cancer research as it currently exists. It will identify and discuss challenges in more comprehensive data collection and how these are being addressed through new initiatives that will enhance the capacity of the registries to support research in a contemporary research setting.
This talk will describe how science is changing as a result of the vast amounts of data we are collecting from gene sequencers to telescopes and supercomputers. This “Fourth Paradigm of Science,” predicted by Jim Gray, is moving at full speed, and is transforming one scientific area after another. The talk will present various examples on the similarities of the emerging new challenges and how Jim Gray’s vision is realized by the scientific community. Scientists are increasingly limited by their ability to analyze the large amounts of complex data available. These data sets are generated not only by instruments but also computational experiments; the sizes of the largest numerical simulations are on par with data collected by instruments, crossing the petabyte threshold this year. The importance of large synthetic data sets is increasingly important, as scientists compare their experiments to reference simulations. All disciplines need a new “instrument for data” that can deal not only with large data sets but the cross product of large and diverse data sets. There are several multi-faceted challenges related to this conversion, e.g., how to move, visualize, analyze, and in general interact with petabytes of data.
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