how about using size constraint in fully supervised setting?

[JunMa11]

Dear @HKervadec ,

Thanks for sharing the code. Really nice work.

Have you tried impose the size constraint in fully supervised segmentation?
In other words, the loss will be CrossEntropy + \lambda C(Vs).

Best,
Jun

[HKervadec]

Hey, We haven't tried yet, but the precise constraints setting can give a rough idea of what to expect. Several interesting aspects of imposing constraints with fully labeled images: * Can alleviate the uncertainty in the labels ; experts don't fully agree on the boundary of the object, often ending up with a DSC of 0.9 between them. However, I they would agree on the size * Can compute higher-orders functions (such [as centroid](https://arxiv.org/abs/1904.04205), and many more) based on those label, and then constraint them as a different way of supervising the training. It is not clear yet how it would fare against pixel-wise supervision, but my personal take is that it could help with generalization power and reduce overfitting * Can simply act as a regularizer This is really easy to try with the current code ; you only need to change either the loss function or its parameters (`idc` from `[1]` to `[0, 1]` for instance).

[JunMa11]

Hi Hoel,

Thanks for your reply and valuable insights very much.

I will try it and get back to you the results.

Best,
Jun