Figure 4

From: Accurate and reproducible invasive breast cancer detection in whole-slide images: A Deep Learning approach for quantifying tumor extent

Figure 4

The most challenging whole-slide image in the CINJ validation cohort achieved the poorest performance via the ConvNetHUP classifier with (A) many FP regions and a Dice coefficient of 0.0745. (B) Some of the FN errors are due to the confounding morphologic attributes of the tumor, arising due to a mixing of IDC with fat cells and irregular, infiltrating looking cribriform glands with DCIS. The FP regions appear to be primarily be due to (C) sclerosing adenosis, and (D) DCIS surrounded by IDC.