ClinNet: Evidential Ordinal Regression with Bilateral Asymmetry and Prototype Memory for Knee Osteoarthritis Grading

TL;DR

ClinNet introduces an evidential ordinal regression framework for knee osteoarthritis grading that models structural asymmetry, maintains class prototypes, and estimates uncertainty, improving accuracy and reliability over traditional methods.

Contribution

The paper presents ClinNet, a novel framework combining bilateral asymmetry modeling, prototype memory, and evidential ordinal regression for more trustworthy KOA grading.

Findings

Achieves a Quadratic Weighted Kappa of 0.892

Outperforms state-of-the-art baselines significantly

Effectively flags out-of-distribution samples

Abstract

Knee osteoarthritis (KOA) grading based on radiographic images is a critical yet challenging task due to subtle inter-grade differences, annotation uncertainty, and the inherently ordinal nature of disease progression. Conventional deep learning approaches typically formulate this problem as deterministic multi-class classification, ignoring both the continuous progression of degeneration and the uncertainty in expert annotations. In this work, we propose ClinNet, a novel trustworthy framework that addresses KOA grading as an evidential ordinal regression problem. The proposed method integrates three key components: (1) a Bilateral Asymmetry Encoder (BAE) that explicitly models medial-lateral structural discrepancies; (2) a Diagnostic Memory Bank that maintains class-wise prototypes to stabilize feature representations; and (3) an Evidential Ordinal Head based on the Normal-Inverse-Gamma (NIG) distribution to jointly estimate continuous KL grades and epistemic uncertainty. Extensive experiments demonstrate that ClinNet achieves a Quadratic Weighted Kappa of 0.892 and Accuracy of 0.768, statistically outperforming state-of-the-art baselines (p < 0.001). Crucially, we demonstrate that the model's uncertainty estimates successfully flag out-of-distribution samples and potential misdiagnoses, paving the way for safe clinical deployment.