Systems and methods for analyzing uncertainty in a multi-scale interpretation of a medical image

Abstract

A method for analyzing uncertainty in a multi-scale interpretation of a medical image includes inputting the medical image into a trained segmentation network. The method includes outputting via the trained segmentation network a segmentation output mask for each pixel of the medical image or a region of interest of the medical image. The method includes utilizing a deterministic function to aggregate segmentation output masks for all pixels of the medical image or the region of interest and to output a first classification prediction of the aggregated segmentation output masks. The method includes inputting the medical image into a trained classification network. The method includes outputting a second classification prediction of the medical image or the region of interest. The method includes determining an uncertainty between the first classification prediction and the second classification prediction via comparison of the first classification prediction to the second classification prediction.