Neural network training protocol for automated image analysis for classifying foreground and background regions

Abstract

Labeling protocols for training datasets and systems and methods for classifying objects of interest and identifying backgrounds. A method includes partitioning an input image into a plurality of grid units and processing the plurality of grid units with an artificial intelligence and/or machine learning (AI/ML) engine to output a classified image, wherein the classified image comprises: one or more background grid units assigned a background classification, and one or more foreground grid units assigned a foreground classification. The method includes calculating an explicit loss function based upon the classified image and a target classification image, wherein the target classification image comprises a background boundary and a foreground boundary. The method includes applying the explicit loss function to the AI/ML engine.

Dependencies are inferred from the claim text.