Reducing outcome variance is an essential task in deep learning based medical
image analysis. Bootstrap aggregating, also known as bagging, is a canonical
ensemble algorithm for aggregating weak learners to become a strong learner.
Random forest is one of the most powerful machine learning algorithms before
deep learning era, whose superior performance is driven by fitting bagged
decision trees (weak learners). Inspired by the random forest technique, we
propose a simple bagging ensemble deep segmentation (BEDs) method to train
multiple U-Nets with partial training data to segment dense nuclei on
pathological images. The contributions of this study are three-fold: (1)
developing a self-ensemble learning framework for nucleus segmentation; (2)
aggregating testing stage augmentation with self-ensemble learning; and (3)
elucidating the idea that self-ensemble and testing stage stain augmentation
are complementary strategies for a superior segmentation performance.
Implementation Detail: https://github.com/xingli1102/BEDs.