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Abstract

Generation of high-performance machine learning models often requires significant computational resources and access to extensive training datasets. This makes development of such models for rare or novel diseases, where diagnostic imagery or other training data is limited, difficult. Methods are provided to apply extensive generic medical imagery training datasets to train machine learning models to embed input medical imaging data into generically informative embedding spaces. Relatively smaller training datasets specific to a novel or rare disease can then be used to develop high-performance models by updating the parameters of the pre-trained generic model and/or by training a smaller, task-specific model to predict one or more variables of interest based on embedding vectors output from the pre-trained generic model. The functionality of such a generic model can be made available via an online service to facilitate development of such task-specific models by smaller research groups.

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First Claim