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Challenges are being increasingly viewed as a mechanism to foster advances in a number of domains, including healthcare and medicine. The US Federal government, as part of the open government initiative, has underscored the role of challenges as a way to "promote innovation through collaboration and (to) harness the ingenuity of the American Public." Large quantities of publicly available data and cultural changes in the openness of science have now made it possible to use these challenges and crowdsourcing (enlisting the services of people via the Internet) efforts to propel the field forward.

The infrastructure requirements to both host and participate in some of the "big data" efforts can be monumental. Medical imaging data can be large, historically requiring the shipping of disks to participants. The computing resourcing needed to process these large datasets may be beyond what is available to individual participants. For the organizers, creating the infrastructure that is secure, robust, and scalable can require resources beyond the reach of many researchers. These resources included , such as IT manpower support, compute capability, and domain knowledge, that are beyond the reach of many researchers.

The medical imaging community has conducted a host of challenges at conferences such as MICCAI (Medical Image Computing and Computer Assisted Intervention) and SPIE (the international society for optics and photonics). However, these have typically have been modest in scope (both in terms of data size and number of participants). Medical imaging data poses additional challenges to both participants and organizers. For organizers, ensure ensuring that the data are is free of PHI is both critical and non-trivial. Medical data is typically acquired in DICOM format. However, ensuring that a DICOM file is free of PHI requires domain knowledge and specialized software tools. Multimodal imaging data can be extremely large. Imaging formats for pathology images can be proprietary and interoperability between formats can require additional software development efforts. Encouraging non-imaging researchers (e.g. for example, machine learning scientists) to participate in imaging challenges can be difficult due to the domain knowledge required to convert medical imaging into a set of feature vectors. For participants, access to large compute clusters with computing power, storage space, and bandwidth can prove difficult. Medical imaging data is challenging for non-imaging researchers.

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