Masked complex non-decimated wavelet features for patient-level classification of contrast-enhanced mammography

Abstract

Contrast-enhanced spectral mammography (CESM) acquires two images of each breast, a low-energy image and a recombined contrast image, but two questions central to building a classifier on them remain unsettled: whether the two image types carry comparable malignancy signal, and how a patient's several images should be combined into a single decision. Both are hard to answer reliably, because most published CESM classifiers split cross-validation folds at the image level, letting images of the same patient fall in both training and test sets and inflating reported performance. We pair a masked complex non-decimated wavelet feature bank with an elastic-net logistic classifier, evaluated under repeated patient-grouped nested cross-validation with patient-cluster bootstrap inference on the CDD-CESM dataset (1,880 images, 308 patients); under this leakage-free evaluation the inflation from testing on previously seen patients is negligible. On normal-versus-malignant detection, the two acquisitions are statistically indistinguishable in patient-level AUC under the proposed evaluation framework. Under single-image fusion the contrast image reaches a patient-level AUC of 0.874 (95% CI 0.827-0.918) and the low-energy image is statistically indistinguishable from it, yet the two encode malignancy through disjoint, interpretable channels: phase coherence on the low-energy image and magnitude distribution on the contrast image. The framework matches a pretrained ResNet-50 representation at the patient level, but whereas the frozen deep representation is not directly interpretable at the level of individual predictors, every predictor in the wavelet representation carries an explicit physical meaning. The result is a transparent, leakage-free baseline against which future CESM classifiers can be measured.