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Found 2 papers, 1 papers with code
Exploring Uncertainty Measures in Deep Networks for Multiple Sclerosis Lesion Detection and Segmentation
We present the first exploration of multiple uncertainty estimates based on Monte Carlo (MC) dropout [4] in the context of deep networks for lesion detection and segmentation in medical images.
Adversarially Learned Mixture Model
The Adversarially Learned Mixture Model (AMM) is a generative model for unsupervised or semi-supervised data clustering.
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