Class Distance Weighted Cross Entropy Loss for Classification of Disease Severity

• URL: http://arxiv.org/abs/2412.01246v3
• Date: Mon, 13 Jan 2025 16:07:46 GMT
• Title: Class Distance Weighted Cross Entropy Loss for Classification of Disease Severity
• Authors: Gorkem Polat, Ümit Mert Çağlar, Alptekin Temizel,
• Abstract summary: We propose a novel loss function, Class Distance Weighted Cross-Entropy (CDW-CE) It penalizes misclassifications more severely when the predicted and actual classes are farther apart. Our results show that CDW-CE consistently improves performance in ordinal image classification tasks.
• Score: 2.7574609288882312
• License:
• Abstract: 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.

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