Loss functions for image segmentation
@article{LossOdyssey,
title = {Loss Odyssey in Medical Image Segmentation},
journal = {Medical Image Analysis},
volume = {71},
pages = {102035},
year = {2021},
author = {Jun Ma and Jianan Chen and Matthew Ng and Rui Huang and Yu Li and Chen Li and Xiaoping Yang and Anne L. Martel}
doi = {https://doi.org/10.1016/j.media.2021.102035},
url = {https://www.sciencedirect.com/science/article/pii/S1361841521000815}
}
Take-home message: compound loss functions are the most robust losses, especially for the highly imbalanced segmentation tasks.
Some recent side evidence: the winner in MICCAI 2020 HECKTOR Challenge used DiceFocal loss; the winner and runner-up in MICCAI 2020 ADAM Challenge used DiceTopK loss.
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Most of the corresponding tensorflow code can be found here.
So I think that the correct way to translate this Sensitivity into **prediction ** and **ground_truth ** terms for a loss function would be similar to:
true_positives = prediction*ground_truth
false_negatives = (1-prediction)*ground_truth
But in the implementation:
Sensitivity = (square(ground_truth-prediction) * (1-one_hot) ) / (1-one_hot)
How should I interpret its expressions(The same occurs with the Specificity )？
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