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  • 标题:Categorized contrast enhanced mammography dataset for diagnostic and artificial intelligence research
  • 本地全文:下载
  • 作者:Rana Khaled ; Maha Helal ; Omar Alfarghaly
  • 期刊名称:Scientific Data
  • 电子版ISSN:2052-4463
  • 出版年度:2022
  • 卷号:9
  • 期号:1
  • 页码:1-10
  • DOI:10.1038/s41597-022-01238-0
  • 语种:English
  • 出版社:Nature Publishing Group
  • 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 .
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