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Deep learning

Learning Entangled Decision Forests for Discriminative Atlases Enabling Rapid High-Resolution Image Interpretation

We develop core machine learning algorithms that have improved learning efficiency, higher test accuracy, use less memory, and faster prediction speeds.One way is by endowing traditional decision forests with deep (cumulative) learning capabilities. This can be achieved through entanglement. This is the use of learned tree structure and intermediate probabilities from nodes in shallow forest levels to help train nodes in deeper forest levels.

Diagram Figure

Deep learning in the decision forest. This example illustrates entanglement which is the use of learned tree structure and intermediate probabilities from nodes in shallow forest levels to help train nodes in deeper forest levels. Shown on the left are the raw image features, in the center are the maximum a-posteriori (MAP) class entanglement feature, which is selected by the forest more and more at deeper levels of tree growth, as shown on the right.
 

We entangle the binary tests applied at each node so that the test can depend on the result of tests applied earlier in the same tree.

One implementation of this is the use of class posteriors of shallower nodes as input features to train deeper nodes. Such a mapClass features learns a discriminative contextual atlas, which provides greater and greater discriminative power as the tree is grown deeper and deeper.