Side-level versus patient-level evaluation in four-view mammography classification: a comprehensive benchmark on the...

Deep learning for mammographic image classification yields impressive performance metrics, but inconsistent evaluation methodologies-specifically whether results are reported at the independent side level or the bilateral patient level-make cross-study comparisons unreliable. The aim of this study was to quantify, on a single dataset and with a uniform training recipe, how much of the reported performance is determined by evaluation granularity rather than by model architecture.

We benchmarked six backbone architectures (ResNet-18, ResNet-50, EfficientNet-B0, DenseNet-121, ConvNeXt-Tiny, ViT-B/16) crossed with three multi-view fusion strategies (concatenation, bilateral asymmetry, cross-view spatial attention) on the biopsy-confirmed Chinese Mammography Database (CMMD; 706 four-view patients), using five-fold patient-level stratified cross-validation. Sixteen configurations completed training for both binary malignancy diagnosis and five-class BI-RADS assessment. We report side-level and patient-level metrics; statistical analyses include 5-fold Wilcoxon signed-rank tests, DeLong's paired AUC test on pooled per-case scores, and bootstrap 95% confidence intervals.

Side-level AUC exceeded patient-level AUC by an average of 17.5 percentage points (range 12.7-22.4), an effect that dwarfs the absolute differences observed between CNN backbones (<3 AUC points). DeLong tests resolved approximately half of all CNN-vs-CNN pairwise comparisons at p<0.05 despite small effect sizes, whereas ViT-B/16 underperformed every CNN variant by 8-10% AUC despite having 6-10× more parameters. Patient-level multi-class BI-RADS evaluation under the standard probability-averaging aggregation rule returned a degenerate macro-AUC of exactly 0.000-a property of the metric/aggregation pair, not of the models-and three concrete alternative aggregators are proposed. The extreme patient-level malignancy prevalence intrinsic to this biopsy-confirmed cohort (96.2%) rendered all models unable to identify non-malignant patients at the patient level.

Reporting methodology, evaluation granularity, and dataset composition are compounding confounds in mammography classification research. Absolute performance numbers reported on CMMD should not be extrapolated to population screening settings, where prevalence is several orders of magnitude lower; studies should report both side-level and patient-level metrics with mutually consistent label/aggregation rules, and characterise performance using confidence intervals or paired statistical tests rather than fold-level Wilcoxon alone.