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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 United States 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 efforts to propel the field forward.

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Some of the key advantages of challenges over conventional methods include 1) scientific rigor (sequestering the test data), 2) comparing methods on the same datasets with the same, agreed-upon metrics, 3) allowing computer scientists without access to medical data to test their methods on large clinical datasets, 4) making resources available, such as source code, and 5) bringing together diverse communities (that may traditionally not work together) of imaging and computer scientists, machine learning algorithm developers, software developers, clinicians, and biologists.

However, despite this potential, there are a number of challenges. Medical data is usually governed by privacy and security policies such as HIPPA that make it difficult to share patient data. Patient health records can be very difficult to completely de-identify. Medical imaging data, especially brain MRIs, can be particularly challenging as one could easily reconstruct a recognizable 3D model of the subject.

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The medical imaging community has conducted a host of challenges at conferences such as MICCAI and SPIE. 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 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. 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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