Omni-supervised learning: Scaling up to large unlabelled medical datasets
Two major bottlenecks in increasing algorithmic performance in the field of medical imaging analysis are the typically limited size of datasets and the shortage of expert labels for large datasets. This paper investigates approaches to overcome the latter via omni-supervised learning: a special case...
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Springer Verlag
2018
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