Image classification using machine learning including weakly-labeled data

A computer-implemented method for training a machine learning model comprising:

• receiving a plurality of digital histology images, wherein each of the plurality of digital histology images is labeled with a respective image-level classification;
• extracting a plurality of tiles from each of the plurality of digital histology images, wherein each of the plurality of tiles is labeled with a respective image-level classification associated with its respective digital pathology image;
• creating a first dataset comprising the plurality of tiles and the respective image-level classifications;
• training a first machine learning model using the first dataset, wherein the trained first machine learning model is configured to output a respective classification and a respective uncertainty measure for each of the plurality of tiles;
• creating a second dataset by sampling the first dataset based on the respective classifications and the respective uncertainty measures output by the trained first machine learning model;
• and
• training a second machine learning model using the second dataset, wherein the trained second machine learning model is configured for classify one or more tiles of a digital histology image.