This method is a type of dimensionality reduction that emphasizes not only the nucleus structure examined at high magnification but also the structural pattern examined at low magnification. Step 1: First, we divide low-magnification pathology images into smaller images, then perform dimensionality reduction using a deep autoencoder followed by weighted non-hierarchical clustering. This process reduces an image with 10-billion-scale pixel data to only 100 feature data with scores. Step 2: Next, we analyze high-magnification images in order to reduce the number of misclassified low-magnification images. Again, we divide these into smaller images, before applying a second deep autoencoder and calculating average scores for the images. Step 3: Results of Step 2 complementarily correct those of Step 1. We remove images in which the results of Steps 1 and 2 do not match. Finally, we use the total numbers of each type of feature to make predictions, for example, to make cancer recurrence predictions, create human-understandable features or automatically annotate images. The color of each region indicates positive (red) and negative (blue) for characteristics detected.