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more dice score definition

See original GitHub issue

Subject of the feature

For the dice, the current definition is It corresponds to the definition in But in, the terms in the denominator are squared.

Two forms are identical in the binary case as the square of 1 is 1. But when y_true and y_pred are probabilities. As far as I know, there’s no closed-form. Both formulations are an approximation.

Inørensen–Dice_coefficient, dice score is defined as 2TP/(2TP+FP+FN). If we consider N i.i.d. voxel with y_true=p, which means with 100*p%, the voxel is foreground. Similarly, let y_pred=q. Then we expect TP=pq*N, FP=(1-p)q*N, FN=p(1-q)*N. This gives the non-squared version.

Therefore it could be nice to provide the square option in case anyone wants it. No need to change the default definition.

Issue Analytics

  • State:closed
  • Created 3 years ago
  • Comments:7 (7 by maintainers)

github_iconTop GitHub Comments

tvercautcommented, Nov 18, 2020

There is no bullet-proof answer to the question of how to implement the soft Dice for deep learning purposes. Different justification lead to different choices (square, non-square, what epsilons and where, foreground only or average between foreground and background dice, how to handle multi-classes, batch version, etc.). Many of the issues arise when you have ground truth with 0 foreground voxels. At least in our experience, the batch version of the soft Dice really helps the training.

If you are interested in that rabbit hole, here are some pointers: Section 3.6.1 in

YipengHucommented, Jan 20, 2021

EDIT: a new ticket may be better - sorry

Read more comments on GitHub >

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