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Fig. 4 | Insights into Imaging

Fig. 4

From: Deep learning workflow in radiology: a primer

Fig. 4

Types of learning. With supervised learning, the number of inputs (CT images in this example) equals numbers of targets (malignancy status of a lesion here). With semi-supervised, the number of inputs is greater than the number of targets (dataset includes unlabeled samples). With unsupervised learning, none of the inputs are labeled (e.g., clustering, manifold learning, restricted Boltzmann machines). N.A. indicates not available information

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