Systems and methods for frame-based validation

A system for validating performance of a trained model configured to predict at least one of a plurality of tissue and/or cellular characteristics categories from a pathology image, the system comprising:

• at least one computer hardware processor;
• and
• at least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by the at least one computer hardware processor, cause the at least one computer hardware processor to perform:
• accessing data representing reference annotations each describing at least one of a plurality of tissue and/or cellular characteristic categories, for one or more frames in a set of frames for one or more pathology images, wherein each frame in the set of frames includes a portion of a pathology image of the one or more pathology images;
• processing, using the trained model, the set of frames to generate model predictions of annotations, each predicted annotations describing at least one of the plurality of tissue and/or cellular characteristic categories, for a processed frame of the set of frames;
• and
• validating performance of the trained model based on the data representing the reference annotations received from at least some of a plurality of users and the model predicted annotations for the set of frames.

At least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by at least one computer hardware processor, cause the at least one computer hardware processor to perform a method for validating performance of a trained model configured to predict at least one of a plurality of tissue and/or cellular characteristics categories from a pathology image, the method comprising:

• accessing data representing reference annotations each describing at least one of a plurality of tissue and/or cellular characteristic categories, for one or more frames in a set of frames for one or more pathology images, wherein each frame in the set of frames includes a portion of a pathology image of the one or more pathology images;
• processing, using the trained model, the set of frames to generate model predictions of annotations, each predicted annotation describing at least one of the plurality of tissue and/or cellular characteristic categories, for a processed frame of the set of frames;
• and
• validating performance of the trained model based on the data representing the reference annotations received from at least some of a plurality of users and the model predicted annotations for the set of frames.