The techniques and systems described herein create and train a multiple clustered instance learning (MCIL) model based on image features and patterns extracted from training images. The techniques and systems separate each of the training images into a plurality of instances (or patches), and then learn multiple instance-level classifiers based on the extracted image features. The instance-level classifiers are then integrated into the MCIL model so that the MCIL model, when applied to unclassified images, can perform image-level classification, patch-level clustering, and pixel-level segmentation.