Add pixelwise loss weight?

[tlnagy]

In the original implementation, they used a weighted loss function to weight up border pixels so that the network learns those preferentially (see Fig 3D below).

Do you have any suggestions for how to implement this in UNet.jl? I'm still really new to Flux so sorry if this is obvious. My guess would be to implement it in loss()

UNet.jl/src/utils.jl

Lines 49 to 52 in 954c89e

	function loss(x, y)
	op = clamp.(u(x), 0.001f0, 1.f0)
	mean(bce(op, y))
	end

EDIT: Here's an implementation of the pixel-wise weights for Keras: https://jaidevd.github.io/posts/weighted-loss-functions-for-instance-segmentation/

[DhairyaLGandhi]

Could we try with a translation first? I am not familiar with their implementation, but seems to be a combination of masking and weighting. Shouldn't be too difficult at all.

[tlnagy]

What do you mean by a translation first?

[DhairyaLGandhi]

I meant translating the loss from keras, sorry I should've been clearer.