Figure 2.

Approaches for automated breast cancer detection. (A–C) Approach 1 (Deep learning-based cell detection combined with distance analysis), approach 2 (“stand alone” gland detection deep learning system), and approach 3 (combination of approach 1 and 2) are shown by visualizing the classification performance in raw images (A,B) and the corresponding visualization of the calcification based on the output data (C). (D,E) Both approach 1 and approach 2 showed a sensitivity and specificity ≥ 0.9. (F) The combined approach 3 performed significantly better in time-depended receiver operating characteristic curves than the raw marker expression (p = 0.006).