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A convolutional deep learning model for improving mammographic breast-microcalcification diagnosis

Authors
 Daesung Kang  ;  Hye Mi Gweon  ;  Na Lae Eun  ;  Ji Hyun Youk  ;  Jeong-Ah Kim  ;  Eun Ju Son 
Citation
 SCIENTIFIC REPORTS, Vol.11(1) : 23925, 2021-12 
Journal Title
SCIENTIFIC REPORTS
Issue Date
2021-12
MeSH
Breast / diagnostic imaging* ; Breast Diseases* / diagnosis ; Breast Diseases* / diagnostic imaging ; Calcinosis* / diagnosis ; Calcinosis* / diagnostic imaging ; Databases, Factual* ; Deep Learning* ; Female ; Humans ; Mammography* ; Models, Theoretical*
Abstract
This study aimed to assess the diagnostic performance of deep convolutional neural networks (DCNNs) in classifying breast microcalcification in screening mammograms. To this end, 1579 mammographic images were collected retrospectively from patients exhibiting suspicious microcalcification in screening mammograms between July 2007 and December 2019. Five pre-trained DCNN models and an ensemble model were used to classify the microcalcifications as either malignant or benign. Approximately one million images from the ImageNet database had been used to train the five DCNN models. Herein, 1121 mammographic images were used for individual model fine-tuning, 198 for validation, and 260 for testing. Gradient-weighted class activation mapping (Grad-CAM) was used to confirm the validity of the DCNN models in highlighting the microcalcification regions most critical for determining the final class. The ensemble model yielded the best AUC (0.856). The DenseNet-201 model achieved the best sensitivity (82.47%) and negative predictive value (NPV; 86.92%). The ResNet-101 model yielded the best accuracy (81.54%), specificity (91.41%), and positive predictive value (PPV; 81.82%). The high PPV and specificity achieved by the ResNet-101 model, in particular, demonstrated the model effectiveness in microcalcification diagnosis, which, in turn, may considerably help reduce unnecessary biopsies.
Files in This Item:
T202124852.pdf Download
DOI
10.1038/s41598-021-03516-0
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Radiology (영상의학교실) > 1. Journal Papers
Yonsei Authors
Gweon, Hye Mi(권혜미) ORCID logo https://orcid.org/0000-0002-3054-1532
Kim, Jeong Ah(김정아) ORCID logo https://orcid.org/0000-0003-4949-4913
Son, Eun Ju(손은주) ORCID logo https://orcid.org/0000-0002-7895-0335
Youk, Ji Hyun(육지현) ORCID logo https://orcid.org/0000-0002-7787-780X
Eun, Na Lae(은나래) ORCID logo https://orcid.org/0000-0002-7299-3051
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/187555
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