A sensitivity analysis of probability maps in deep-learning-based anatomical segmentation.
Journal:
Journal of applied clinical medical physics
Published Date:
Jul 7, 2021
Abstract
PURPOSE: Deep-learning-based segmentation models implicitly learn to predict the presence of a structure based on its overall prominence in the training dataset. This phenomenon is observed and accounted for in deep-learning applications such as natural language processing but is often neglected in segmentation literature. The purpose of this work is to demonstrate the significance of class imbalance in deep-learning-based segmentation and recommend tuning of the neural network optimization objective.