  Shortage of annotated data is one of the greatest bottlenecks in biomedical
image analysis. Meta learning studies how learning systems can increase in
efficiency through experience and could thus evolve as an important concept to
overcome data sparsity. However, the core capability of meta learning-based
approaches is the identification of similar previous tasks given a new task - a
challenge largely unexplored in the biomedical imaging domain. In this paper,
we address the problem of quantifying task similarity with a concept that we
refer to as task fingerprinting. The concept involves converting a given task,
represented by imaging data and corresponding labels, to a fixed-length vector
representation. In fingerprint space, different tasks can be directly compared
irrespective of their data set sizes, types of labels or specific resolutions.
An initial feasibility study in the field of surgical data science (SDS) with
26 classification tasks from various medical and non-medical domains suggests
that task fingerprinting could be leveraged for both (1) selecting appropriate
data sets for pretraining and (2) selecting appropriate architectures for a new
task. Task fingerprinting could thus become an important tool for meta learning
in SDS and other fields of biomedical image analysis.
