Learning to diagnose from scratch by exploiting dependencies among labels

ICLR 2018 Li YaoEric PoblenzDmitry DaguntsBen CovingtonDevon BernardKevin Lyman

The field of medical diagnostics contains a wealth of challenges which closely resemble classical machine learning problems; practical constraints, however, complicate the translation of these endpoints naively into classical architectures. Many tasks in radiology, for example, are largely problems of multi-label classification wherein medical images are interpreted to indicate multiple present or suspected pathologies... (read more)

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