# Class Distance Weighted Cross Entropy Loss for Classification of Disease Severity

arXiv: https://arxiv.org/abs/2412.01246 · HTML: https://mlsanity.com/paper/2412.01246/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 35 references that are in the map.
- First posted on arXiv 2 Dec 2024.

## Builds on (most cited first, 10 of 10)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Searching for MobileNetV3](https://mlsanity.com/paper/1905.02244/) (2019)
- [CosFace: Large Margin Cosine Loss for Deep Face Recognition](https://mlsanity.com/paper/1801.09414/) (2018)
- [SphereFace: Deep Hypersphere Embedding for Face Recognition](https://mlsanity.com/paper/1704.08063/) (2017)
- [NormFace: L2 Hypersphere Embedding for Face Verification](https://mlsanity.com/paper/1704.06369/) (2017)
- [Rank consistent ordinal regression for neural networks with application to age estimation](https://mlsanity.com/paper/1901.07884/) (2019)
- [Deep Neural Networks for Rank-Consistent Ordinal Regression Based On Conditional Probabilities](https://mlsanity.com/paper/2111.08851/) (2021)
- [Class Distance Weighted Cross-Entropy Loss for Ulcerative Colitis Severity Estimation](https://mlsanity.com/paper/2202.05167/) (2022)
- [A simple squared-error reformulation for ordinal classification](https://mlsanity.com/paper/1612.00775/) (2016)

## Built on by (most cited first, 7 of 7)

- [Learning Disease State from Noisy Ordinal Disease Progression Labels](https://mlsanity.com/paper/2503.10440/) (2025)
- [Enabling Conversational Behavior Reasoning Capabilities in Full-Duplex Speech](https://mlsanity.com/paper/2512.21706/) (2025)
- [Reliable Conformal Prediction for Ordinal Classification Using the Ranked Probability Score](https://mlsanity.com/paper/2606.24959/) (2026)
- [CLoE: Curriculum Learning on Endoscopic Images for Robust MES Classification](https://mlsanity.com/paper/2508.13280/) (2025)
- [Every Error has Its Magnitude: Asymmetric Mistake Severity Training for Multiclass Multiple Instance Learning](https://mlsanity.com/paper/2603.13682/) (2026)
- [MAOL: Morphology-Aware Ordinal Learning for Fine-Grained Industrial Defect Severity Grading](https://mlsanity.com/paper/2609.02266/) (2026)
- [Adaptive Margin Ordinal Loss: Penalizing Center-Class Hedging in Ordinal Classification](https://mlsanity.com/paper/2609.10752/) (2026)

## Abstract (arXiv)

Assessing disease severity with ordinal classes, where each class reflects increasing severity levels, benefits from loss functions designed for this ordinal structure. Traditional categorical loss functions, like Cross-Entropy (CE), often perform suboptimally in these scenarios. To address this, we propose a novel loss function, Class Distance Weighted Cross-Entropy (CDW-CE), which penalizes misclassifications more severely when the predicted and actual classes are farther apart. We evaluated CDW-CE using various deep architectures, comparing its performance against several categorical and ordinal loss functions. To assess the quality of latent representations, we used t-distributed stochastic neighbor embedding (t-SNE) and uniform manifold approximation and projection (UMAP) visualizations, quantified the clustering quality using the Silhouette Score, and compared Class Activation Maps (CAM) generated by models trained with CDW-CE and CE loss. Feedback from domain experts was incorporated to evaluate how well model attention aligns with expert opinion. Our results show that CDW-CE consistently improves performance in ordinal image classification tasks. It achieves higher Silhouette Scores, indicating better class discrimination capability, and its CAM visualizations show a stronger focus on clinically significant regions, as validated by domain experts. Receiver operator characteristics (ROC) curves and the area under the curve (AUC) scores highlight that CDW-CE outperforms other loss functions, including prominent ordinal loss functions from the literature.