If dividing pic into many N_subimgs is better than using whole pic to train? about retina-unet HOT 3 CLOSED

Hi Bonsen,

I think using sub-images instead of the whole pictures is way better, because training on only 20 images would be very hard. Moreover, by extracting partially overlapping sub-images you obtain a significant augmentation of the data. At early stages of development I did few experiments with full images, but with very poor results.

As @pythonokai said, the "2" in the code introduce the two classes: positive (blood vessel) and negative (no blood vessel). I did it only to adapt the data to the keras model, but maybe there's a better way to do it :)

from retina-unet.

Hi, Bonsen,

first I am not the initial developer, @lantiga @dcorti they are.
And if you don't mind, here is my understanding, If there is any error, please correct me:

g-net(just like a Gaussian curve?) is a model derived from u-net, added upsampling and maxpool,
it seems that the AUC of ROC on DRIVE being increased by 0.1%,
but the cost is 3x training time of u-net.

And function 'masks_Unet' show that mask_patches(1-channel) have been turn to 2-channel,
to save the 'Negative-channel' and the 'Positive-channel',
I think it just want to adapt the data to the model's output then fit.

And next time, added the code from and from line is better. : )
Hope it can help you.

from retina-unet.

@pythonokai @dcorti
THANKS!

from retina-unet.