@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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20231101 | Bingyuan Liu | Do we really need dice? The hidden region-size biases of segmentation losses (pytorch) | MedIA |
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201612 | Md Atiqur Rahman | Optimizing Intersection-Over-Union in Deep Neural Networks for Image Segmentation (paper) | 2016 International Symposium on Visual Computing |
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201605 | Zifeng Wu | TopK loss Bridging Category-level and Instance-level Semantic Image Segmentation (paper) | arxiv |
201511 | Tom Brosch | "Sensitivity-Specifity loss" Deep Convolutional Encoder Networks for Multiple Sclerosis Lesion Segmentation (code) | MICCAI 2015 |
201505 | Olaf Ronneberger | "Weighted cross entropy" U-Net: Convolutional Networks for Biomedical Image Segmentation (paper) | MICCAI 2015 |
201309 | Gabriela Csurka | What is a good evaluation measure for semantic segmentation? (paper) | BMVA 2013 |
Most of the corresponding tensorflow code can be found here.