Image segmentation with 3 classes but one of them is easy to find, How can I write the network to not train on the easy one?
I'm not sure if you can do that. I think You should apply some regularization and/or dropout to the network and feed it more data.
But what you could do is to label all the empty pixels as noise as signal is usually in the middle and noise is on the outer side of the signal graph. Then you train the network that way. You will have to set network outputs to 2: noise or signal. From the original image you know which pixels were empty and then you can set those pixels from noise to empty. Then you will have the result you wanted. 
The only thing that can happen here is that the  network will perform bad because of the imbalanced classes as you will have much more noise than signal pixels.
I am using MS-D or UNet network for image segmentation. My image has three classes: noise, signal and empty. The class empty is easy to find because the pixel values for the empty class is mainly -1 while for the two other classes is between 0-1. 
Is there a way that I only ask the network to find noise and signal class and not bother the network about the easy one? Or any other clue that can help? I am seeing that the network sometimes is confused when predicting the signal pixels and gives about the same score but with higher to the signal class (e.g. empty0.0001, noise0.0003, signal0.0005) to all three classes. I want to make it easier for the network to figure it out.
Just more information about my image, around 25% of pixels are signal, 40% noise, and 35% are empty. I am using dice_coef for the metric and loss function.