Fig. 1: AIDmap workflow.

HALO deep learning neural network was trained to recognize morphological structures in H&E whole-slides images (WSIs) (details in methods section). 1: The first classifier was trained to annotate the fibroglandular tissue (stroma), leaving adipocytes outside (green line). 2: DCIS classification was applied within the annotated stroma, by detecting pixels that reached more than 90% of confidence of composing a DCIS duct (red areas in the image heatmap). 3: Next, a nuclear segmentation sensing hematoxylin staining was applied within the DCIS regions to detect all nuclear structures. After these three steps, HALO provided tables containing the area, perimeter and spatial coordinates of stroma, DCIS and nuclear objects that were imported to R studio. 4: A True/False computational filtering was applied according to the nuclear perimeter, area and circular shape factor in order to eliminate false nuclear objects. And a True/False filtering was applied on DCIS objects, according the density of cells and average minimal nuclear distance (min. nucl. dist.) within the duct, to eliminate false DCIS ducts detected by HALO. Finally, morphological measurements for each DCIS duct were obtained.