Using computer-assisted perception to determine the characteristics of missed and reported breast cancers
Claudia R. Mello-Thoms, STANLEY M. DUNN, Calvin F. Nodine, Harold L. Kundel, Susan P. Weinstein · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2001
Early detection of breast cancer is the desired goal in breast cancer screening. Nonetheless it has been reported in the literature that 10-30% of all breast cancers are missed by the radiologist, albeit most of these are deemed visible in retrospect on the mammogram. In this work we have studied the underlying structure of the areas that attracted the radiologist's visual attention and either yield or do not yield a response. We have shown that the spatial frequency profile of areas where a lesion is detected (TP) is significantly different from the one where a lesion is missed (FN), where a lesion is incorrectly placed (FP) or of lesion-free areas that are correctly identified (TN). Furthermore, we have shown that the spatial frequency profile alone can be used by an artificial neural network to predict decision outcome in that area of the image.