Inproceedings,
VISCERAL: Towards Large Data in Medical Imaging - Challenges and Directions
MCBR-CDS MICCAI workshop, volume 7723 of Springer LNCS, Nice, France, (2013)
Abstract
The increasing amount of medical imaging data acquired in
clinical practice holds a tremendous body of diagnostically relevant information.
Only a small portion of these data are accessible during clinical
routine or research due to the complexity, richness, high dimensionality
and size of the data. There is consensus in the community that leaps
in this regard are hampered by the lack of large bodies of data shared
across research groups and an associated de nition of joint challenges on
which development can focus. In this paper we describe the objectives of
the project Visceral. It will provide the means to jump start this process
by providing access to unprecedented amounts of real world imaging
data annotated through experts and by using a community e ort to generate
a large corpus of automatically generated standard annotations.
To this end, Visceral will conduct two competitions that tackle large
scale medical image data analysis in the elds of anatomy detection, and
content based image retrieval, in this case the retrieval of similar medical
cases using visual data and textual radiology reports.
