Beyond the Tumor Signature: Investigating Global Mammographic Context in Machine-Learning–Assisted Breast Imaging
TL;DR
Whether focusing a traditional machine-learning pipeline on an annotated lesion improves classification of benign versus malignant abnormalities improves classification of benign versus malignant abnormalities is tested.
Abstract
Artificial intelligence (AI) is increasingly studied for mammographic image analysis, but model performance depends on preprocessing, dataset design, and evaluation choices. This study tested whether focusing a traditional machine-learning pipeline on an annotated lesion improves classification of benign versus malignant abnormalities. A Kaggle-distributed copy of the mini-MIAS mammography dataset was used, with dataset identity and annotation definitions checked against the MIAS documentation [1]. Abnormal mammograms with valid lesion coordinates were analyzed using two strategies: the whole mammogram and a lesion-centered region of interest (ROI). Both used identical Histogram of Oriented Gradients (HOG) features [2] and class-weighted logistic regression. Performance was evaluated with 10 repeats of 5-fold stratified, patient-grouped cross-validation. Whole-mammogram processing produced mean ROC-AUC 0.571 (95% CI 0.540–0.602), compared with 0.532 (95% CI 0.496–0.567) for ROI processing. The paired ROI-minus-whole difference was −0.039 (95% CI −0.080–0.001; paired t-test p=0.055). Sensitivity was 0.537 for whole images and 0.498 for ROI images. Lesion-focused cropping therefore did not improve this HOG-plus-logistic-regression