9. A system for image recognition analysis of a digital image comprising: a memory having program instructions and data storage space; a processor configured to use the program instructions to perform the steps of: generating a plurality of characterized pixels from a digitized pathology image; determining in a first layer feature analysis a plurality of first layer confidence scores based on the plurality of characterized pixels, wherein the confidence scores represent a first likelihood of each pixel belonging to a specific classification; determining in a second layer feature analysis a plurality of final recognition scores based on the plurality of characterized pixels and the plurality of first layer confidence scores by applying a second layer model to the plurality of first layer confidence scores, wherein the second layer model is selected from a plurality of second layer models each of which is targeted to perform a specific classification task, wherein the recognition scores represent a final likelihood of each pixel belonging to a specific classification; and classifying part or all of the digitized pathology image based on the final recognition scores, wherein each pixel of the plurality of pixels is labeled with a ground truth; each first layer model from among a plurality of first layer models is generated by machine-learning algorithms based on a correspondence between the ground truth of each pixel of the plurality of pixels and feature descriptor values of a feature of each pixel of the plurality of pixels corresponding to a designated feature type from among a plurality of feature types; and a different first layer model is generated to correspond to each feature type from among the plurality of feature types.
9. A system for image recognition analysis of a digital image comprising: a memory having program instructions and data storage space; a processor configured to use the program instructions to perform the steps of: generating a plurality of characterized pixels from a digitized pathology image; determining in a first layer feature analysis a plurality of first layer confidence scores based on the plurality of characterized pixels, wherein the confidence scores represent a first likelihood of each pixel belonging to a specific classification; determining in a second layer feature analysis a plurality of final recognition scores based on the plurality of characterized pixels and the plurality of first layer confidence scores by applying a second layer model to the plurality of first layer confidence scores, wherein the second layer model is selected from a plurality of second layer models each of which is targeted to perform a specific classification task, wherein the recognition scores represent a final likelihood of each pixel belonging to a specific classification; and classifying part or all of the digitized pathology image based on the final recognition scores, wherein each pixel of the plurality of pixels is labeled with a ground truth; each first layer model from among a plurality of first layer models is generated by machine-learning algorithms based on a correspondence between the ground truth of each pixel of the plurality of pixels and feature descriptor values of a feature of each pixel of the plurality of pixels corresponding to a designated feature type from among a plurality of feature types; and a different first layer model is generated to correspond to each feature type from among the plurality of feature types. 10. The system of claim 9 , wherein the processor configured for characterizing each pixel of a plurality of the pixels is further configured for: generating an image classification confidence map representing the final image classification likelihood based on the plurality of final recognition scores; and classifying part or all of the digitized pathology image based on the image classification confidence map.