In the paper "A NOVEL FOCAL TVERSKY LOSS FUNCTION WITH IMPROVED ATTENTIONU-NETFOR LESION SEGMENTATION" the author use deep supervision by outputing multiple outputmask which have different scale.
I do not understand how it can work with regards to the loss function. y_pred and y_true doesnt share the same dimension exept for the final output.
model = Model(inputs=[img_input], outputs=[out6, out7, out8, out9])
the input seems to only be the one with real resolution
I checked the code (https://github.com/nabsabraham/focal-tversky-unet/blob/master/newmodels.py) and I haven't see anything special that would make it works. The loss function doesn't explicitly handle it neither.