Quantifying effect-specific mammographic density.
A methodology is introduced for the automated assessment of structural changes of breast tissue in mammograms. It employs a generic machine learning framework and provides objective breast density measures quantifying the specific biological effects of interest. In several illustrative experiments on data from a clinical trial, it is shown that the proposed method can quantify effects caused by hormone replacement therapy (HRT) at least as good as standard methods. Most interestingly, the separation of subpopulations using our approach is considerably better than the best alternative, which is interactive. Moreover, the automated method is capable of detecting age effects where standard methodologies completely fail.
Raundahl
Jakob
J
University of Copenhagen, Department of Computer Science, Denmark.
Loog
Marco
M
Pettersen
Paola
P
Nielsen
Mads
M
D015502:Absorptiometry, Photon / Q000379:methods*; D000465:Algorithms; D001185:Artificial Intelligence; D001943:Breast Neoplasms / Q000000981:diagnostic imaging*; D005260:Female; D006801:Humans; D008327:Mammography / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012720:Severity of Illness Index