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Robust and efficient medical imaging with self-supervision
Google AI Blog ai.googleblog.com
Despite recent progress in the field of medical artificial intelligence (AI), most existing models are narrow, single-task systems that require large quantities of labeled data to train. Moreover, these models cannot be easily reused in new clinical contexts as they often require the collection, de-identification and annotation of site-specific data for every new deployment environment, which is both laborious and expensive. This problem …
annotation artificial artificial intelligence collection computer vision data de-identification deployment engineer environment google google research health identification imaging intelligence laura medical medical artificial intelligence medical imaging progress research self-supervised learning supervision systems