Early-grade collapse in deep knee osteoarthritis grading: imbalance-aware training under controlled ordinal regularization.
Authors
Affiliations (1)
Affiliations (1)
- Actuarial Sciences, Selcuk Universitesi Fen Fakultesi, Selçuk Üniversitesi, Alaeddin Keykubat Yerleşkesi, Fen Fakültesi, Aktüerya Bilimleri Bölümü (Dekanlık Binası - Kat:3), Selçuklu, Konya, Konya, 42130, Turkey.
Abstract
Automated radiographic grading of knee osteoarthritis is summarized by accuracy or quadratic weighted kappa (QWK), which can hide failure at Kellgren-Lawrence (KL) Grade 1. We studied this early-grade collapse using ImageNet-pretrained EfficientNet-B4 models (380 pixels) under a fixed protocol on the official Knee Osteoarthritis Dataset with Severity split. File names follow a seven-digit-identifier-plus-side pattern consistent with participant identifiers; interpreted this way, the split is subject-disjoint (4130 identifiers, both knees in the same split). A five-seed 2 × 2 factorial design crossed weighted cross-entropy with mild class-balanced sampling; a five-seed paired comparison evaluated CORAL-style ordinal regularization with pairwise ranking. QWK, the primary endpoint, did not differ detectably between configurations (0.824-0.838). Plain cross-entropy recalled only 0.118 ± 0.068 of Grade-1 knees. Class weighting and sampling raised Grade-1 recall (main effects +0.091 and +0.083; Holm-adjusted p = 0.016 and 0.029), reducing Grade-1 specificity (-0.034 and -0.029). Threshold-free Grade-1 discrimination was unchanged (average precision 0.350-0.360; AUROC 0.755-0.763), and a validation-selected Grade-1 decision offset applied to plain cross-entropy achieved comparable precision-recall operating points. Ordinal regularization improved no endpoint over the imbalance-aware baseline (QWK difference -0.005, 95% CI -0.019 to 0.008); ordinal weight had no detectable effect across three seeds, while blending ordinal-head probabilities monotonically lowered Grade-1 recall. Validation-tuned class-bias correction improved validation accuracy in all six runs but reduced test accuracy in four, left it unchanged in one, and improved it in one (mean test change -0.006). Retraining after removing perceptual-hash near-duplicate candidates did not change performance. Within this benchmark, early-grade collapse is largely a decision-threshold phenomenon: imbalance handling shifts the Grade-0/Grade-1 operating point without improving Grade-1 separability. Grade-1 sensitivity and specificity should accompany aggregate agreement.