Machine learning for digital pathology

A method comprising:

• a) training an untrained machine learnable device to predict status of a diagnostic, prognostic, or theragnostic feature in stained tissue samples, the untrained machine learnable device being trained with a characterized set of digital images of stained tissue samples, each digital image of the characterized set having a known status for the diagnostic, prognostic, or theragnostic feature and an associated 2-dimensional grid of spatial locations, training of the untrained machine learnable device including steps of:
• identifying a plurality of extracted features in each digital image of the characterized set of digital images;
• associating a value for each extracted feature with each spatial location to form a set of feature maps, each extracted feature map providing values for a extracted feature over the associated 2-dimensional grid of spatial locations;
• and
• inputting the set of extracted feature maps to the untrained machine learnable device to form associations therein between the set of extracted feature maps and the known status for the diagnostic, prognostic, or theragnostic feature thereby creating a trained machine learnable device;
• and
• b) predicting a status for the diagnostic, prognostic, or theragnostic feature of a stained tissue sample of unknown status for the diagnostic feature by:
• obtaining a sample digital image for the stained tissue sample, the digital image having an associated 2-dimensional grid of spatial locations;
• associating a value for each extracted feature with each spatial location of the digital image to form a test set of extracted feature maps for the stained tissue sample of unknown status;
• and
• inputting the test set of extracted feature maps to the trained machine learnable device to obtain a predicted status for the status of the diagnostic, prognostic, or theragnostic feature for the stained tissue sample.