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In this paper we aim to refine the concept of grand challenges in medicalimage analysis, based on statistical principles from quantitative andqualitative experimental research. We identify two types of challenges based ontheir generalization objective: 1) a deployment challenge and 2) an insightchallenge. A deployment challenge's generalization objective is to findalgorithms that solve a medical image analysis problem, which thereby requiresthe use of a quantitative experimental design. An insight challenge'sgeneralization objective is to gain a broad understanding of what class ofalgorithms might be effective for a class of medical image analysis problems,in which case a qualitative experimental design is sufficient. Both challengetypes are valuable, but problems arise when a challenge's design and objectiveare inconsistent, as is often the case when a challenge does not carefullyconsider these concepts. Therefore, in this paper, we propose a theoreticalframework, based on statistical principles, to guide researchers in challengedesign, documentation, and assessment. Experimental results are given thatexplore the factors that effect the practical implementation of thistheoretical framework.

A Framework for Challenge Design: Insight and Deployment Challenges to Address Medical Image Analysis Problems