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Reducing outcome variance is an essential task in deep learning based medicalimage analysis. Bootstrap aggregating, also known as bagging, is a canonicalensemble algorithm for aggregating weak learners to become a strong learner.Random forest is one of the most powerful machine learning algorithms beforedeep learning era, whose superior performance is driven by fitting baggeddecision trees (weak learners). Inspired by the random forest technique, wepropose a simple bagging ensemble deep segmentation (BEDs) method to trainmultiple U-Nets with partial training data to segment dense nuclei onpathological 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 augmentationare complementary strategies for a superior segmentation performance.Implementation Detail: https://github.com/xingli1102/BEDs.

BEDS: Bagging ensemble deep segmentation for nucleus segmentation with testing stage stain augmentation