TY - JOUR
T1 - Deep Learning in Mammography and Breast Histology, an Overview and Future Trends
AU - Hamidinekoo, Azam
AU - Denton, Erika R. E.
AU - Rampun, Yambu Andrik
AU - Honnor, Kate
AU - Zwiggelaar, Reyer
PY - 2018/7/1
Y1 - 2018/7/1
N2 - Recent improvements in biomedical image analysis using deep learning based neural networks could be exploited to enhance the performance of Computer Aided Diagnosis (CAD) systems. Considering the importance of breast cancer worldwide and the promising results reported by deep learning based methods in breast imaging, an overview of the recent state-of-the-art deep learning based CAD systems developed for mammography and breast histopathology images is presented. In this study, the relationship between mammography and histopathology phenotypes is described, which takes biological aspects into account. We propose a computer based breast cancer modelling approach: the Mammography-Histology-Phenotype-Linking-Model, which develops a mapping of features/phenotypes between mammographic abnormalities and their histopathological representation. Challenges are discussed along with the potential contribution of such a system to clinical decision making and treatment management
AB - Recent improvements in biomedical image analysis using deep learning based neural networks could be exploited to enhance the performance of Computer Aided Diagnosis (CAD) systems. Considering the importance of breast cancer worldwide and the promising results reported by deep learning based methods in breast imaging, an overview of the recent state-of-the-art deep learning based CAD systems developed for mammography and breast histopathology images is presented. In this study, the relationship between mammography and histopathology phenotypes is described, which takes biological aspects into account. We propose a computer based breast cancer modelling approach: the Mammography-Histology-Phenotype-Linking-Model, which develops a mapping of features/phenotypes between mammographic abnormalities and their histopathological representation. Challenges are discussed along with the potential contribution of such a system to clinical decision making and treatment management
KW - mammography
KW - breast histopathology
KW - computer aided diagnosis
KW - deep learning
U2 - 10.1016/j.media.2018.03.006
DO - 10.1016/j.media.2018.03.006
M3 - Article
C2 - 29679847
SN - 1361-8415
VL - 47
SP - 45
EP - 67
JO - Medical Image Analysis
JF - Medical Image Analysis
ER -