Keras implementation of 'Learning Iterative Processes with Recurrent Neural Networks to Correct Satellite Image Classification Maps'

This paper uses a 2 stage process of training where th results from a CNN are used to train a RNN for refinement. The RNN is analogous to a diffusion process in the form of a PDE. The RNN takes advantage of a MLP being a universal function approximator to solve the system in a x_{t + 1} = x_{t} + \delta x_{t}.

This implementation uses a U-Net like model to generate heatmaps and then a RNN is built by stacking layers that share weights.

#Results Sample results can be seen in the sample-results folder. Note that the image on the left is the segmentation generated by my own CNN from my UltraSoundNerveSegmentation repo. The right is the result of taking that segmentation and the original image and running it through the iterative RNN for refinement.

It is clear that the RNN is reducing noise in the segmentation and even clears up false positives as seen in Figure 8.