Batch normalization not used? Step2 dataset? about cascaded-fcn HOT 3 CLOSED

No specific reason to not use BatchNorm layers, except for lack of time. Let us know if you found them useful.

The other points :

1. Your intuition is probably correct. This is however what we experimented with, so far. You contribution is welcome.
2. In step1 and step2, to account for the imbalance problem we used class weights, giving more weight to the liver or tumor pixels, because there are way more background pixels.

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Thank you so much for your reply.
Yes, batch normalization helps a lot. It fastens convergence.
To overcome imbalance problem, I use dice coefficient instead, I found it quite useful.
In caffe and TensorFlow, for some reasons, the batch size is limited to 1 or 2, but with Keras, batch size could be up to 16. This also helps a lot.
@juliandewit, during his big work with DSB2017 challenge, he used 2d u-net with Keras. He also changed a little the architecture. I think this may help. Also a little trick: use batch normalization before conv layer.

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