摘要：Contrast-enhanced spectral mammography (CESM) is a relatively recent imaging modality with increased diagnostic accuracy compared to digital mammography (DM) . New deep learning (DL) models were developed that have accuracies equal to that of an average radiologist . However, most studies trained the DL models on DM images as no datasets exist for CESM images . We aim to resolve this limitation by releasing a Categorized Digital Database for Low energy and Subtracted Contrast Enhanced Spectral Mammography images (CDD-CESM) to evaluate decision support systems . The dataset includes 2006 images, with an average resolution of 2355 × 1315, consisting of 310 mass images, 48 architectural distortion images, 222 asymmetry images, 238 calcifcations images, 334 mass enhancement images, 184 non-mass enhancement images, 159 postoperative images, 8 post neoadjuvant chemotherapy images, and 751 normal images, with 248 images having more than one fnding . This is the frst dataset to incorporate data selection, segmentation annotation, medical reports, and pathological diagnosis for all cases . Moreover, we propose and evaluate a DL-based technique to automatically segment abnormal fndings in images .
