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Shortage of annotated data is one of the greatest bottlenecks in biomedicalimage analysis. Meta learning studies how learning systems can increase inefficiency through experience and could thus evolve as an important concept toovercome data sparsity. However, the core capability of meta learning-basedapproaches is the identification of similar previous tasks given a new task - achallenge largely unexplored in the biomedical imaging domain. In this paper,we address the problem of quantifying task similarity with a concept that werefer to as task fingerprinting. The concept involves converting a given task,represented by imaging data and corresponding labels, to a fixed-length vectorrepresentation. In fingerprint space, different tasks can be directly comparedirrespective of their data set sizes, types of labels or specific resolutions.An initial feasibility study in the field of surgical data science (SDS) with26 classification tasks from various medical and non-medical domains suggeststhat task fingerprinting could be leveraged for both (1) selecting appropriatedata sets for pretraining and (2) selecting appropriate architectures for a newtask. Task fingerprinting could thus become an important tool for meta learningin SDS and other fields of biomedical image analysis.

Task Fingerprinting for Meta Learning in Biomedical Image Analysis