Reliable quality assurance of X-ray mammography scanner by evaluation the standard mammography phantom image using an interpretable deep learning model.

Journal: European journal of radiology
Published Date:

Abstract

OBJECTIVE: Mammography is the initial examination to detect breast cancer symptoms, and quality control of mammography devices is crucial to maintain accurate diagnosis and to safeguard against degradation of performance. The objective of this study was to assist radiologists in mammography phantom image evaluation by developing and validating an interpretable deep learning model capable of objectively evaluating the quality of standard phantom images for mammography.

Authors

  • Jang-Hoon Oh
    Department of Biomedical Science and Technology, Graduate School, Kyung Hee University, Seoul, Korea.
  • Hyug-Gi Kim
    Department of Biomedical Engineering, Graduate School, Kyung Hee University, 1732, Deogyeong-daero, Giheunggu, Yongin-si, Gyeonggi-do 446-701, Korea.
  • Kyung Mi Lee
    Department of Radiology, Kyung Hee University College of Medicine, Kyung Hee University Hospital, #23 Kyungheedae-ro, Dongdaemun-gu, Seoul 02447, Republic of Korea.
  • Chang-Woo Ryu
    Department of Radiology, Kyung Hee University Hospital at Gangdong, College of Medicine, Kyung Hee University 892 Dongnam-ro, Gangdong-Gu, Seoul-05278, Korea.