Implementation of the Class Distance Weighted Cross-Entropy Loss in PyTorch. This loss is designed for the multilabel classification problems, when one assumes ordinal nature between the classes.
The CDW Cross-Entropy Loss is presented in the Class Distance Weighted Cross-Entropy Loss for Ulcerative Colitis Severity Estimation (see citations) and further extended in the Using sequences of life-events to predict human lives.
This repository provides a simple implementation of original CDW Cross-Entropy and the one used in the life2vec case.
from cdw_cross_entropy_loss import CDW_CELoss
loss = CDW_CELoss(num_classes = 4,
alpha = 2., # Weight or penalty term
delta = 3., # Only used for the Huber Transform
reduction = "mean",
transform = "log", # Original paper uses power transform
eps = 1e-8)@inproceedings{polat2022class,
title={Class distance weighted cross-entropy loss for ulcerative colitis severity estimation},
author={Polat, Gorkem and Ergenc, Ilkay and Kani, Haluk Tarik and Alahdab, Yesim Ozen and Atug, Ozlen and Temizel, Alptekin},
booktitle={Annual Conference on Medical Image Understanding and Analysis},
pages={157--171},
year={2022},
organization={Springer}
}@article{savcisens2024using,
author={Savcisens, Germans and Eliassi-Rad, Tina and Hansen, Lars Kai and Mortensen, Laust Hvas and Lilleholt, Lau and Rogers, Anna and Zettler, Ingo and Lehmann, Sune},
title={Using sequences of life-events to predict human lives},
journal={Nature Computational Science},
year={2024},
month={Jan},
day={01},
volume={4},
number={1},
pages={43-56},
issn={2662-8457},
doi={10.1038/s43588-023-00573-5},
url={https://doi.org/10.1038/s43588-023-00573-5}
}@misc{life2vec_code,
author = {Germans Savcisens},
title = {Official code for the "Using Sequences of Life-events to Predict Human Lives" paper},
note = {GitHub: SocialComplexityLab/life2vec},
year = {2023},
howpublished = {\url{https://doi.org/10.5281/zenodo.10118621}},
}